Pre-Ictal Seizure Prediction from EEG Using an Attention-Augmented Temporal Learning Framework
There are approximately 50 million people worldwide living with epilepsy, highlighting the need for strong early warning systems to enable prompt clinical management. Despite the high performance of recently developed deep learning models based on Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM/GRU) networks, they are highly sensitive to ictal features and are not very effective in detecting the subtle temporal transitions leading to seizure onset. This article introduces an attention-improved deep learning model to predict pre-ictal seizures using EEG signals. The suggested model combines CNN-based feature extraction, Bi-LSTM/GRU temporal sequence modelling, and a learnable temporal attention mechanism to identify the early neural dynamics before the onset of seizures. The experimental dataset was constructed from four publicly available EEG repositories following the proposed temporal labeling strategy and comprises EEG recordings from 25 selected patients, 243 annotated seizure events, and 2,847 hours of continuous EEG recordings, using a patient-wise stratified 5-fold cross-validation protocol. Experimental evaluation demonstrates that the proposed framework achieves a predictive accuracy of 93% (±1.2%), sensitivity of 90% (±1.4%), specificity of 90% (±1.1%), an AUC-ROC of 0.93 (±0.012), a PR-AUC of 0.961 (±0.009), and a low false alarm rate of 0.12 ± 0.015 per hour. To confirm statistical reliability, all reported metrics are validated across 10 independent runs and supplemented with 95% confidence intervals and paired Wilcoxon signed-rank tests (p < 0.05) against all baselines. Attention weight analysis verifies that the model selectively targets temporally informative pre-ictal EEG regions and improves clinical interpretability.
Diversity-Preserving Few-Shot GAN Adaptation via Layer-Aware and Similarity-Guided Meta-Training Samples
Few-shot learning is a significant challenge for Meta-GANs. Without explicit mechanisms to preserve diversity, the model tends to overfit to the scarce training data. We build on a Reptile-based meta-learning backbone and extend it into a diversity-preserving Meta-GAN framework. Our model aims to improve both the stability and diversity of few-shot adaptation. Although the meta-training phase follows the standard Reptile mechanism, the meta-testing phase introduces several key modifications to address the diversity collapse that is typical of low-data adaptation. We employ a strategy in which one copy of the meta-trained model is reused to induce diversity throughout the meta-test training, whereas another copy is adapted to the target task. To ensure fidelity to the target sample, we use layer freezing and train exclusively on the target sample and meta-training samples during alternating discriminator iterations. Compared to baseline Reptile-MetaGAN experiments on the MNIST and Omniglot datasets in a few-shot configuration yielded improvements in Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS). These findings demonstrate that a stable and diversity-aware pathway for few-shot generative adaptation can be achieved using cross-task diversity meta-train samples, alternating layer freezing, and selective SSIM filtering.
Dual-Model Transfer Learning with Graph Neural Networks and SHAP Explainability for Robust Colorectal Tumor Margin Detection Process
When performing surgery for colorectal cancer, it is essential to precisely delineate the margins of the tumor. This is because even minute errors in the margins can have a significant impact on the quality of the resection, the risk of recurrence, and the planning of therapy following the surgery. When slides are supplied from multiple locations, the computer-aided pathology models now in use do not always work correctly, and their explanations do not always focus on the invasive boundary, which is where clinical decisions are most critical. The purpose of this study is to demonstrate a two-model transfer learning system that combines spatial aggregation based on graph neural networks with SHAP-guided border explainability in order to improve the accuracy of colorectal tumor margin identification. Weighted tissue graphs are created from the slides used in histopathology. While the lines illustrate how the cells are related in space shape by utilizing Delaunay and k-nearest neighbor, the nodes illustrate the areas that are split into cellular or super-pixel regions. At the same time, five modules are utilized: CDGCV, which stands for Cross-Domain Graph Consistency Validation, is a technique that verifies the consistency of the topology between centers. BFSRT, which stands for Boundary-Focused SHAP Refinement Test, is a test that verifies the correctness of boundary-specific explanations. The strength of the structure is evaluated using a technique known as Multi-Scale Relational Perturbation Assessment (MSRPA); Dynamic SHAP-Graph Alignment (DSGA) is used to determine whether t the interpretation is timely, and Hierarchical Graph-SHAP Ensemble (HGSE) is used to determine whether the epithelium, stroma, and invasive-front cells are in accord with one another. Dice scores can reach up to 0.94, boundary fidelity is close to 0.90, cross-center consistency is over 0.82, and F1 degradation is less than 3% when edges are rewired 20%, according to experimental tests conducted on the Kather, CRC-TIA, and internal multi-center cohorts. The support for transferability is increased by testing conducted at an external center. Therefore, the system provides accurate margin localization, stable graph reasoning, and explanations that are comprehensible to medical professionals for the purpose of deploying colorectal pathology across many locations.
Quantum-Inspired Explainable Deep Learning Framework for Hepatocellular Carcinoma Detection Using Gene Expression Data
The Hepatocellular Carcinoma (HCC) diagnosis using traditional machine learning algorithms based on gene expression data faces high dimensionality, nonlinear interactions between genes, and poor interpretability. This paper proposes a quantum-inspired deep learning model to classify tumor and non-tumor liver samples based on transcriptomic profiles. The proposed model combines trigonometric encoding, parameterized nonlinear transformation, and interaction layers of features in a neural learning framework to improve the representation of gene dependencies. It includes an explainability module based on SHAP attribution and gradient-based counterfactual analysis to facilitate gene-wise explanation of predictions. The cohort-based training and independent evaluation are performed on publicly available HCC gene expression data. Performance is measured in terms of classification, calibration and robustness metrics and compared against traditional deep neural models. The findings suggest better predictive performance and predictable probabilistic actions. The proposed model achieves a high accuracy of 95.6%, a low counterfactual impact score of 0.112, a high stability index of 0.79 and the calibration of ECE: 2.03%, Brier score: 0.091.
An Efficient Dual Branch Multi-Scale CNN With Reconstruction Learning and Explainable AI for Dental Caries Classification
Preventive oral healthcare depends on the early detection of dental caries. Delicate visual differences between healthy tooth and early-stage lesions make automated disease diagnosis difficult. This work introduces a comparative analysis between a Single-Branch Convolutional Neural Network (SBCNN) and a proposed Dual-Branch Multi-Scale Convolutional Neural Network (DBMSCNN) for multiple class dental caries classification. It integrates Sharpness-Aware Minimization (SAM), multi-scale feature extraction, and self-supervised reconstruction learning to enhance feature robustness and generalization. We utilized a three-class dental caries dataset consisting of No Enamel Caries, Advanced Caries and Early Stage Caries. A total of 447 samples were used for testing in each fold. The proposed DBMSCNN framework achieved a mean classification accuracy of 92.43% across five-fold stratified cross-validation. The model demonstrated strong discriminative capability and a low false-positive rate, with a mean specificity of 96.14% and a mean precision of 92.59%. Reconstruction-based self-supervised pretraining improved anatomical feature learning, resulting in mean Dice and IoU scores of 0.8530 and 0.7577 respectively. Feature-level analysis using t-SNE is done for qualitative visualization of latent feature distributions for the proposed model, while Grad-CAM and saliency maps show meaningful and moderate localization of carious regions. The comparative results validate the effectiveness of multi-scale learning, reconstruction-based regularization, and SAM optimization in improving diagnostic reliability for dental caries classification.
Region-Specific Adaptive GLCM Framework for Surface Scratch Detection in Heterogeneous Chocolate Products
Automated defect detection on the surface of chocolate products is one of the most important techniques that must be used to ensure the quality and consistency of chocolate products during their manufacturing processes. However, due to the variation in the shapes, textures, and reflectivity of different brands of chocolates, this process can become difficult. To address this issue, a region-based adaptive GLCM approach is proposed for the analysis of textures on the surface of chocolates. Local variance analysis is carried out to segment the images into texture homogeneous regions. Following feature extraction, Principal Component Analysis (PCA)-based transformation is performed for feature transformation and then classified using a Support Vector Machine (SVM). A dataset consisting of 233 original chocolate images from 14 commercial brands, together with augmented training samples, was used to evaluate the proposed framework. The proposed method demonstrated an accuracy of 93.59%, recall of 96.15%, precision of 86.21%, F1-score of 90.91%, and AUC of 97.78% during the multi-brand evaluation. Leave-One-Brand-Out validation results achieved a mean recall of 93.42%, demonstrating its robustness in cross-brand defect detection. The proposed framework provides reliable automated chocolate scratch defect detection while maintaining low computational complexity and high interpretability.
Explainable ViT and SCNN-LSTM Framework for Audio-Assisted Image Captioning
Assistive technologies are crucial for image captioning, a process that involves generating textual captions of visual scenes. Many of the current methods, however, lack semantic understanding, are computationally complex, and are not interpretable. Based on these challenges, in this paper, we present an Explainable Vision Transformer and Depthwise Separable CNN-LSTM (ViT-SCNN-LSTM) architecture to achieve accurate and explainable image caption generation. The framework is based on a pre-trained Vision Transformer (ViT-B/16) to extract rich visual features and a lightweight Depthwise Separable SCNN-LSTM decoder to generate descriptive captions without high computational complexity. Grad-CAM is integrated to get visual explanations by identifying image areas that affect the generation of captions. The audio-assisted module also provides an option to turn captions into speech, making it easier for visually impaired users to access the generated captions. This model was developed using Python and tested on the Flickr8k image corpus (8,000 images, 40,000 captions). To evaluate the generalisation capability, cross-dataset experiments were also performed with Flickr30k and MSCOCO 2017. Experimental results achieved ROUGE-1, ROUGE-L, ROUGE-S, BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of 0.87, 0.86, 0.85, 0.8848, 0.8750, 0.8649, and 0.8547, respectively. The results show the proposed approach offers an efficient, interpretable, and user-friendly solution for automatic image understanding and caption generation.
Quantum Laplacian Pyramid Segmentation for Multi-Class Mushroom Classification
Quantum computing proposes a new paradigm for image analysis by efficiently representing features in high-dimensional Hilbert spaces. In the first stage, we propose a novel Quantum Laplacian Pyramid Segmentation (QLPS) module to segment complex structural mushroom profiles by exploiting a parameter-efficient, single-layer, 4-qubit variational quantum circuit (VQC) with cyclic ring topology. The second stage combines this quantum-refined mask with a raw high-resolution image track using an element-wise Hadamard product to create a background-suppressed region of interest (ROI) that allows a downstream EfficientNetB0 network to focus entirely on clean biological textures. Rigorous experimental evaluations confirm the system objectives, both individually and collectively. We observe that the QLPS module correlates well with the generated ground truth masks, with a mean Pixel Accuracy (mPA) of 93.79% and a Dice coefficient of 94.26%. The integrated classifier achieves a final classification accuracy of 95.88% across four different categories. Most importantly, comparative ablation testing quantifies the direct architectural contribution of the quantum segmentation step to the classification improvement. The removal of background clutter through the QLPS branch produces a significant 9.56% classification improvement relative to standard classical networks trained on raw, noisy images.
A Hybrid CNN-Based Attention Model for Weed Recognition Under Variable Field Conditions with Edge Device Simulations
Biotic stress significantly affects agricultural productivity, and weed infestation remains a major factor limiting crop yields and resource-use efficiency. Traditional weed management tools and practices are less effective and labor-intensive at scale. Therefore, automated and intelligent weed identification and classification technologies are required to manage weed infestation. Recent deep learning studies have shown promising results for weed classification. Still, many deep learning architectures have large parameter counts and require high computational power, limiting their deployment in real-world agricultural environments. In this study, a novel lightweight deep learning model, Lightweight Hybrid Channel-Spatial Attention (L-HCSA), is presented for crop-weed image classification in a real-world agricultural environment. L-HCSA is built on a pre-trained MobileNetV2 backbone, enhanced with a hybrid channel-spatial attention module to handle background noise and enhance the discriminative power of weed features. The CWD30 database is used to benchmark the L-HCSA model. An ablation study was performed on five configurations, confirming that the HCSA attention block is the primary contributor to performance. The HCSA attention block adds 0.41 million parameters to the MobileNetV2 baseline (2.63 million parameters) and improves accuracy by 2.18%. The L-HCSA is compared against nine baseline models, including MobileNetV2, MobileNetV3-L, DenseNet-121, ResNet-50, ResNet-101, EfficientNetV2-M, EfficientNet-Lite, TinyViT, and EdgeNeXt. The L-HCSA model has only 3.04 million trainable parameters (12.1 MB) and achieves 77.34% accuracy with a ROC-AUC of 0.9955. A pairwise Wilcoxon signed-rank test with Bonferroni correction and Cohen’s d effect size was performed for statistical evaluation. The L-HCSA model is evaluated for real-time performance, meeting the >=10 FPS threshold in an embedded hardware simulation while maintaining competitive accuracy, achieving 22.0 FPS on the Google Coral Edge TPU.
Explainable Artificial Intelligence with Deep Representation Learning Model for Polycystic Ovary Syndrome Detection Using Clinical Features
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine condition affecting females and requires timely diagnosis for appropriate management and to mitigate long-term adverse problems. It is a complex condition identified by medical features such as hormonal imbalance, the existence of numerous ovarian follicles, irregular menstrual cycles, and anovulation. Over the past few years, Artificial Intelligence and deep learning approaches have demonstrated positive outcomes in medical applications by allowing effective analysis of intricate medical data. This study introduces an Explainable Artificial Intelligence-based Deep Representation Learning Model for Polycystic Ovary Syndrome Detection (XAIDRL-PCOSD). The proposed approach aims to support early and reliable detection of PCOS by leveraging real-world patient data. The model applies a hybrid feature selection strategy combining mutual information, recursive feature elimination, and LASSO to recognize the most significant clinical attributes. For classification, a feature tokenization transformer is employed to learn deep feature representations and capture complex relationships among clinical parameters. During training, the Ranger optimizer is utilized to enhance convergence and overall model performance. In addition, explainable artificial intelligence is incorporated using SHAP to provide clear insights into feature contributions, enhancing the interpretability of the model’s predictions for both PCOS and No PCOS cases. The experimental outcomes show that the proposed model accomplishes effective performance in PCOS detection with an accuracy of 95.71% while also offering transparency in decision-making. Therefore, the proposed XAIDRL-PCOSD model can serve as an effective tool in assisting medical professionals in understanding key factors influencing diagnosis.
CNN-LSTM Model for Effective Glaucoma Detection from Fundus Images
Glaucoma is one of the main causes of irreversible blindness, where delayed detection may increase the risk of permanent damage to the optic nerve. Developing accurate and computationally efficient computer-aided diagnostic systems using low-cost retinal fundus images remains challenging, especially with multi-disease datasets and limited clinical metadata. This paper proposes a hybrid CNN-LSTM multi-model for detecting glaucoma and six others ophthalmic diseases by merging retinal fundus images with clinically annotated patient symptoms. The model was trained and evaluated using the ODIR-5K dataset and expert-guided symptom annotation. A lightweight convolutional neural network was employed to extract visual features, while a Long Short-Term Memory network processed textual symptom descriptions. The extracted features were fused for classification across seven classes. The model achieved an accuracy of 92%, a macro-average F1-score of 74.7%, a weighted-average F1-score of 92%, and a macro-averaged AUC of 96.8% across seven predicted classes. Then, Explainable AI (XAI) techniques were applied to verify that diagnostic decisions were mainly driven by clinically relevant retinal regions. These results reveal that integrating visual and symptom-based information can improve retinal disease classification while maintaining computational efficiency, making the proposed framework suitable for deployment in limited-resource environments.
Wrapper-Based Adversarial Input Screening for Deep Image Classifiers Using Feature Squeezing and Logit-Space Inconsistency
Adversarial perturbations pose a practical integrity risk to vision-based decision systems by causing image classifiers to misclassify inputs after visually subtle changes. This paper evaluates a wrapper-based adversarial input screening approach that compares a classifier’s output on an original image with its output after benign feature-squeezing transformations. The evaluated transformations include median filtering, non-local means denoising, and bit-depth reduction. Using a frozen-backbone ResNet50 on CIFAR-10, the detector is assessed under untargeted and targeted Fast Gradient Sign Method attacks, followed by stronger Projected Gradient Descent verification. Detection thresholds are calibrated only on clean data using a fixed 5% false positive rate protocol and are validated on held-out clean samples. The results show that softmax-space ℓ1 inconsistency provides moderate, transformation-dependent detection, whereas logit-space ℓ2 inconsistency yields a stronger, more stable screening signal. Median filtering with logit-space ℓ2 achieves near-complete detection at ε = 8/255 and remains the most reliable configuration across the perturbation sensitivity analysis, while non-local means denoising becomes effective mainly for large perturbations. An additional two-stage gate is evaluated for deployment-oriented alert triage; it does not replace the primary detector or override its screening decision, but ranks flagged inputs by risk severity. Further evaluations show that performance decreases on TinyImageNet and that targeted threshold-aware adaptive optimisation can substantially reduce detection recall. The findings support the use of median filtering with logit-space ℓ2 inconsistency as a tool for screening adversarial inputs to image classifiers, but its effectiveness depends on dataset complexity, classifier behaviour, and attack adaptivity.
ASO‑Optimized EfficientNet‑B2 with CBAM for Brain Tumor MRI Classification
Magnetic resonance imaging (MRI) plays a critical role in brain tumor diagnosis; however, accurate classification remains a challenging task in medical image analysis. Gradient-based optimizers form the foundation of deep learning models; however, they are susceptible to suboptimal local minima and hyperparameter sensitivity. This paper proposes a hybrid model in which the CBAM attention module, the classifier head, and the final two convolutional blocks of EfficientNet-B2 are optimized using Atom Search Optimization (ASO), while the remaining backbone layers are retained as fixed feature extractors. ASO generates a population of candidate solutions by simulating atomic motion using Lennard-Jones and bond-length potentials, without relying on gradient information, and is used to search the high-dimensional parameter space. Experiments on the Figshare brain tumor dataset (3,064 T1-weighted MRI images of glioma, meningioma, and pituitary tumors) demonstrate that the ASO-optimized EfficientNet-B2+CBAM model has an accuracy of 99.35% and F1-score of 99.38%, outperforming Adam (98.76%) and SGD (98.12%). Statistically significant improvements (p < 0.01) are observed, and convergence analysis indicates ASO is less susceptible to plateaus of loss, and the variance is reduced by 44.7% across runs. The effectiveness of ASO is further evaluated through ablation studies, ROC analysis, and Grad-CAM visualizations, demonstrating its potential as an alternative optimizer for brain tumor MRI classification. Further validation on multi-center clinical datasets is required to assess generalizability and clinical applicability.
Multistage Preprocessing and Ensemble Classification for Diabetic Retinopathy Detection
Diabetic retinopathy (DR) is a leading cause of preventable blindness among people with diabetes and demands improved automated screening. The principal challenge addressed here is the variability of fundus-image quality, which limits the reliability of automated DR detection. A multistage image enhancement and classification framework is proposed. First, the input image is preprocessed by dark-border cropping, circular retinal region extraction, Ben Graham's weighted-Gaussian contrast enhancement, and Contrast Limited Adaptive Histogram Equalization (CLAHE) on the CIELAB L-channel, followed by resizing to 640×640 pixels. Second, a fine-tuned ResNet50V2 network with appended Global-Average-Pooling, dropout, and dense layers produces a 2048-dimensional feature vector. Third, a two-stage ensemble of RBF-kernel SVMs is used: (i) five one-vs-rest classifiers covering all DR classes and (ii) three additional SVMs targeting the intermediate grades (Mild, Moderate, Severe), combined by a four-rule confidence-scored decision system. All SVM hyperparameters are tuned by 5-fold stratified cross-validation on a training subset, and features are standardised (z-score) before classification. On the Kaggle DR Detection dataset, using an approximate 81% for training 9% for validation and 10% for testing. The model achieves an overall test accuracy of 83% and an F_1-score of 0.91 for the No DR class. The proposed hybrid deep learning and SVM approach performs competitively for grading diabetic retinopathy, and also offers transparency in terms of evaluation.
Morphological Transition Flow-based Feature Extraction for Five-Class Diabetic Retinopathy Classification
Diabetic retinopathy threatens the vision of 103 million people globally, yet computational and infrastructural barriers continue to block automated screening where it is needed most. We propose RF-MTF, a three-module framework evaluated on 34,860 EyePACS fundus images across five ICDR grades (validation set n=6,972). Module 1 applies AMS-CLAHE with Gabor morphological guidance. Module 2 introduces the Morphological Transition Flow — RGB channels processed by random projection networks (ELM, RVFL, BLS) with dual-scale morphological operations, yielding 384-dimensional feature vectors. Module 3 benchmarks three classifiers across 34 configurations spanning four optimization levels. Preprocessing variants produced near-identical F1-scores (0.9045–0.9086, Δ=0.41 pp) despite large image quality divergence, questioning the assumption that preprocessing optimization is critical for DR classification. The optimal configuration reached F1-weighted=0.9701, F1-macro=0.8910, AUC-ROC=0.9941. Random Forest achieved exceptional configuration-wise stability (σ=0.0002), in contrast to XGBoost's high optimization sensitivity (σ=0.2097) and SVM's persistent low performance on this feature space (σ=0.0108, F1≈0.25 across all configurations). Inference runs in 1–3 ms within a 20–40 MB footprint. RF-MTF delivers competitive 5-class ICDR classification on CPU hardware (F1-weighted=0.9701, F1-macro=0.8910), with minority-class performance (Grade 4 F1=0.78, 95% CI [0.70, 0.85], n=70) reflecting the inherent statistical challenges of evaluating rare classes under the natural 95.9:1 class imbalance of real-world DR screening populations — identified as the primary minority-class improvement target. External validation on Messidor-2, APTOS 2019, and IDRiD is the immediate next step.
Contrast and Visibility Enhancement of Weather-Degraded Images Using Dual-Branch CNN and Transformer with Perceptual Loss for ADAS
Weather can be either poor or good. Poor weather, includING fog, hazE, rain, or low light, can cause dramatic degradation of image perception in road-level situations, leading to with significant performance loss in camera-based Advanced Driver-Assistance Systems (ADAS), Although traditional improvement techniques relying on Convolutional Networks (CNNs) cannot effectively preserve global context in image appearance improvement, techniques using transformers show high computational costs. This restricts their application as real-time system efficiency becomes critically important. In this paper, we propose a solution using the Dual-Branch CNN Transformer, which uniformly utilizes localized spatial features extraction together with global semantic modeling using parallel experience sharing of Convolutional Networks and Self Attention Mechanisms. An adaptive gated fusion module integrates these complementary local and global representations through learnable spatial weighting, while perceptual-loss-guided optimization emphasizes texture fidelity, structural consistency, and visual realism. The model was tested on real-world driving image datasets such as BDD100K and KITTI Foggy Datasets and compared with state-of-the-art dehaze networks and general weather condition restoration networks. The proposed model achieved a PSNR of 36.5 dB, an SSIM of 0.962, and an LPIPS of 0.081 while recording an inference latency of 42 ms/frame, corresponding to 23.8 FPS (~24 FPS) on an NVIDIA RTX 4090 GPU. Qualitative evaluation further demonstrated improved restoration of lane boundaries, vehicle contours, and overall scene coherence under adverse weather conditions. These findings indicate that the proposed framework provides an efficient and perceptually robust solution for visibility enhancement in autonomous driving scenarios.
Improving Temporal Localization in Vision-Language Video Anomaly Detection
The recent developments in the vision-language-based model framework for weakly supervised video anomaly detection (WSVAD) have significantly enhanced anomaly detection performance. The dual-branch framework consisting of two branches for performing binary classification and aligning textual descriptors with visual snippets, has been found efficient concerning anomaly detection. Nonetheless, a significant problem of temporal localization still persists. The existing solutions use a fixed value of top-k snippets without consideration for either short or long anomalies. Moreover, prediction inconsistency in terms of temporal localization with the presence of spikes in normal periods and gaps in anomalies is another issue that arises. The current solution is based on the VadCLIP framework and only modifies some specific aspects of it. First, Confidence-Adaptive MIL (CA-MIL) computes a per-video threshold from the score distribution, selecting fewer snippets when confidence lowers and more when an anomalous event has a larger time frame. Second, a temporal smoothness term penalizes abrupt score transitions between adjacent snippets. Third, two parallel scoring heads, one point-wise MLP, and one local-context convolution are fused through learned gating that accounts for disagreement. Lastly, at test time, Score-level Temporal Context Aggregation (STCA) smooths the final predictions using local averaging and global statistics. Cross-modal attention provides a small additional boost to AUC. In UCF-Crime, the average mAP between the 0.1–0.5 IoU thresholds increases from 6.68 to 9.37 (+40.3%), with mAP@0.5. XD-Violence sees an average increase in mAP from 24.70 to 31.63 (+28.1%). Detection performance is preserved (UCF-Crime AUC decreases by 0.10% from 88.02 to 87.92; XD-Violence AP increases by 0.22% from 84.51 to 84.73).
Copyright (c) 2026 Journal of Innovative Image Processing
A Hybrid ConvNeXtV2–MaxViT Framework with CNN-based Feature Refinement for Skin Lesion Classification
Accurate and prompt detection of skin disorders, particularly malignant skin cancers like melanoma, is considered crucial for effective treatment and improved clinical outcomes. It is a difficult task for even experienced dermatologists to correctly distinguish between similar skin lesions. Deep neural architectures, like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), can be used for the automated classification of abnormalities in dermoscopic images. ViTs require a large amount of data for optimal generalization, while CNNs are less effective at identifying global patterns. A hybrid model that overcomes the limitations of CNNs and ViTs is proposed in this work. A CNN-based feature refinement module is included in the proposed system to improve lesion-focused features while suppressing irrelevant background information. A dual-path classification algorithm utilizing ConvNeXtV2 for efficient local feature identification and MaxViT to model broader contextual relationships is then employed. The proposed architecture was evaluated on the HAM10000 dataset and validated on the ISIC dataset. The proposed model outperforms single CNN, ViT, and classical CNN-ViT combination models, based on the experimental results. The architecture discussed here achieves an accuracy of 96.8%, an AUC of 0.978, and a balanced F1-score of 0.965 on the HAM10000 dataset, while demonstrating competitive performance when validated on the ISIC dataset. The effect of CNN-based feature refinement has also been studied. These results demonstrate the effectiveness of combining CNN-based feature refinement with multi-scale feature identification to develop robust and accurate systems for skin disease classification.
Deep Denoising with Dual-Branch Confidence-Aware Network for Diabetic Retinopathy Classification
Diabetic retinopathy (DR) is a condition resulting from prolonged increased blood sugar that destroys the retinal blood vessels. Early diagnosis could prevent permanent and complete blindness. Mostly, retinal fundus images are broadly utilized for the detection and localization of intra-retinal limitations. This article offers a Hybrid Deep Denoising and Dual-Branch Confidence-Aware Classification for Accurate Diabetic Retinopathy (HDD-DBCADR) from retinal images. The main purpose of the HDD-DBCADR framework is the combination of a hybrid denoising network and confidence-aware dual-branch classification, which offers a promising direction for developing reliable and clinically applicable automated DR screening systems. The proposed model involves the design of a hybrid deep denoising network, which integrates a residual convolutional neural network with an autoencoder-based refinement module to enhance image quality. For robust feature representation, a dual-branch architecture is applied, where the local branch exploits a multi-scale convolutional neural network for deriving fine-grained spatial features, and the global branch leverages a dual attention vision transformer to model long-range dependencies and contextual data. At last, a dual-branch confidence-aware classification mechanism adaptively combines predictions from both branches, depending on their confidence scores, thereby improving classification reliability. The simulation investigation of the HDD-DBCADR approach is performed using a benchmark DR detection dataset from the Kaggle repository. Extensive comparative results report the encouraging performance of the proposed HDD-DBCADR model against recent state-of-the-art models with models. achieving a maximum accuracy of 98.96% and SSIM of 0.9760. The inclusion of the denoising network with the deep learning-based DR classification model accomplishes enhanced diagnostic performance, making it useful for real-time clinical settings.
A Dual-Backbone CNN Framework with Gated Cross-Attention Fusion and Mixup Focal Loss for Robust and Explainable Classification of Brain Tumors
Diagnosing and classifying brain tumors based on MRI scans is a difficult and complicated process due to differences in tumor types, low contrast, and class imbalance. In this study, we propose a framework that uses a dual-backbone convolutional neural network (CNN) featuring EfficientNetV2-S and ConvNeXt-Tiny for multi-scale feature extraction and classification. The gated cross-attention fusion module enables adaptive bidirectional interaction among the feature representations. In addition, Mixup regularization, focal loss, and label smoothing improve robustness, calibration, and class imbalance handling. The model employs a two-stage transfer learning framework, supported by exponential moving average (EMA)-based stabilization. The framework achieves 95.73% classification accuracy, a macro-F1 score of 95.7%, and a macro-average ROC–AUC of 0.9967 when evaluated on a multi-class MRI brain tumor dataset containing 7,023 images. The proposed fusion and training strategy demonstrate effectiveness through comparative and ablation analyses. Furthermore, Grad-CAM visualizations show that the model focuses on tumor-related regions. The proposed dual-backbone gated cross-attention framework for brain tumor classification demonstrates both high performance and good interpretability. The model also shows potential for clinical decision-support systems but requires additional multi-center validation.
A Four-Dimensional Chaotic Map for Secure Image Encryption and Data Embedding
This study proposes a full system that can protect digital images during transmission over the network while addressing the shortcomings of existing image encryption schemes. The suggested method combines encryption and steganography using a 4D hyper-chaotic map, which generates pseudo-random sequences that are operationally employed to encrypt the secret message using the XOR operation and dynamically choose the pixel positions for embedding the message with LSB substitution. The evaluation system assesses performance in terms of PSNR, SSIM, encryption time, key-space analysis, NIST statistical tests, NPCR, and UACI. The findings from experiments show that the proposed system has a key space of more than 2512 indicating strong security. The images generated by the method, known as stego images, show very high quality. The PSNR values of the stego images range from 46.93 to 68.07 dB, and the SSIM values range from 0.892 to 0.995. The core encryption process takes no more than 1.19×10−5 seconds for key generation and XOR-based encryption, while the embedding process takes ~10−3 seconds. The proposed scheme has been confirmed to be robust through analysis. The NIST Statistical Test demonstrates that the data obtained is highly random in nature. Furthermore, the NPCR value obtained is more than 99.6% and UACI value is more than 33.4%. Thus, the data is highly resistant to the differential attacks. In conclusion, the major contribution of this study is the use of a 4D hyper-chaotic map for providing secure encryption and dynamic embedding. The proposed system achieves a good balance between security, computational efficiency, and image quality, indicating that the proposed model can achieve secure real-time image transmission.
An Empirical Evaluation and Comparative Study of Metaheuristic-Optimized Deep Learning for Four-Class Retinal Disease Classification
Traditional retinal disease diagnosis using colour fundus imaging is subject to inter- and intra-observer variability and subjective bias. Meanwhile, manual or heuristic hyperparameter selection for neural network training is vulnerable to suboptimal convergence. There are also common challenges, including the lack of standardized evaluation protocols, non-uniform experimental designs, and class imbalance, which further hinder the development of reliable classifiers. This research addresses these limitations by employing metaheuristic algorithms to optimize the training of AlexNet, GoogleNet, and EfficientNet-B0. Particle swarm optimization (PSO), grey wolf optimization (GWO), and wild horse optimization (WHO) are evaluated for their effectiveness in identifying optimal training hyperparameter configurations for a four-class fundus image classification task. Statistical analysis revealed no significant association between the choice of optimization algorithm and the prediction performance (ρ = 0.341). The difference is more pronounced when testing the relationship between the network architecture and the prediction outcomes (ρ = 0.015). Among the evaluated optimizer-architecture pairs, GWO-EfficientNet demonstrates superior performance, achieving an accuracy of 96.52%, precision of 96.50%, recall of 96.47%, specificity of 98.84%, F1 score of 96.48%, and a Matthew’s correlation coefficient (MCC) of 95.33%, outperforming other optimizer-architecture combinations. Repeated experiments show strong performance consistency across the best-performing pairs, with a standard deviation of below 2%. The high accuracy and low inference time highlight the potential of the proposed approach for real-time ophthalmology applications, supporting improved clinical decision-making and more efficient eye care delivery.
Uncertainty-Aware Monocular Depth Estimation for Indoor Scenes Using a Hybrid Bayesian–CNN Framework
Single RGB image monocular depth estimation is an inherently ill-posed task because three-dimensional data is lost when building an image. Despite the recent remarkable performance of deep learning-based models, most existing techniques are based on deterministic models that are weak in considering uncertainty, which restricts their application in complex indoor settings. To overcome this shortcoming, this paper introduces a hybrid probabilistic–deep learning architecture to estimate the depth of indoor monocular scenes, which combines Bayesian uncertainty representation with supervised convolutional neural network (CNN) refinement. As part of the proposed solution, a Bayesian Network is used to model the probabilistic dependence between image features and depth, generating pixel-wise posterior depth distributions that explicitly reflect estimation uncertainty. The following probabilistic depth priors are then used to train a supervised encoder-decoder CNN with an uncertainty-aware loss formulation that allows for smoother predictions of metric depth while retaining uncertainty information. This two-stage approach makes it less sensitive to ambiguous visual information and enhances depth estimation stability at a lower computational cost. Our proposed approach delivers an AbsRel of 0.080, RMSE of 0.290 and RMSE-log of 0.044 on the NYU Depth V2 dataset, along with a threshold accuracy of 93.0% with δ < 1.25. This shows comparable results with enhanced stability and uncertainty prediction for indoor depth estimation.
Hybrid CNN-Transformer for Knee Osteoarthritis Severity Grading
The problem of accurately diagnosing of the degree of severity of knee osteoarthritis (KOA) based on simple radiographic images lies in the difficulty of distinguishing between adjacent degrees and differences in imaging conditions, nad in the ordinal nature of the Kellgren-Lawrence (KL) scoring system. In this paper, we use plain radiography (X-ray anteroposterior knee radiographs) as the main type of imaging for KOA analysis. To solve these problems, we introduce an innovative hybrid architecture based on CNNs and transformers with adaptive feature integration and ordinal-aware dual-head learning for KOA degree of severity diagnosis. The novel architecture incorporates a CBAM-ResNeXt-50 model as the backbone network for texture extraction, along with a lightweight transformer-based encoder for modeling the whole anatomy structure. We effectively integrate local and global semantics by designing a learnable adaptive feature fusion module at the image level, producing stage-aware attention on different KOA degrees. Furthermore, we develop an ordinal-aware dual-head learning paradigm that can jointly conduct KL grade classification and continuous KOA severity regression tasks. Experimental results achieve 96.84% accuracy, 0.96 macro F1-score, 0.21 MAE, and 0.959 macro AUC-ROC with fewer adjacent-grade confusions.
Explainable Embryo Viability Classification Using Multi-Scale Residual Attention Networks
Embryo viability is essential for in vitro fertilization to achieve a successful transfer, implantation, and pregnancy outcome. However, automatic embryo classification remains a difficult task, as microscopy image collections are often limited in number, not balanced between classes, and challenging for clinical staff to read and trust completely. This paper presents a robust binary model for assessing the viability of embryos from images taken during IVF using a microscope. The proposed network is characterized by a hierarchical architecture with convolution, which consists of Conv Block modules, multi-scale residual blocks, depthwise separable convolutions, residual pathways, and spatial attention. The Hung Vuong Hospital Embryo Classification dataset was adopted, consisting of 801 cleaned and annotated images of embryos. Stratification and splitting were applied to the data to create training, validation, and test sets without any leakage. To balance the classes within the training dataset, only the minority class underwent data augmentation, while the other classes remained unchanged. The proposed algorithm was compared with EfficientNetB0, DenseNet121, and ResNet50 based on accuracy, precision, recall, F1 score, confusion matrix, ROC AUC, and PR AUC scores. The latter is particularly relevant when the data is not balanced. Additionally, the use of Grad-CAM heat maps and parameter randomization sanity tests has been applied to determine whether the generated explanations are grounded in consistent performance of the model or only seem reasonable visually. The proposed model achieved an accuracy of 0.96, precision of 0.91, recall of 0.94, F1 score of 0.92, ROC AUC of 0.92, and PR AUC of 0.90. Confusion matrix analysis revealed that the model obtained 11 true positives, 1 false negative, 2 false positives, and 67 true negatives. Consequently, it can be concluded that the proposed framework is capable of delivering accurate embryo viability classifications supported by images that can be interpreted in the context of medical imaging studies with insufficient amounts of data.
A Hybrid Vision Transformer Model for Leukemia Image Classification
This work presents an automated method for distinguishing leukemic blast cells from normal bone marrow cells in microscopic blood smear images. The approach uses a Vision Transformer (ViT) as the main feature extractor, combined with a Kolmogorov–Arnold Network (KAN) representation and an XGBoost classifier for final classification. Typically, a multilayer perceptron (MLP) head is used to classify images in most Vision Transformer architectures. In this study, we explore the potential improvement of transformer feature discernibility for leukemia detection by replacing the conventional MLP representation with a KAN-based transformation. The pre-trained Vision Transformer processes CLS-token embeddings extracted from the images. Subsequently, these embeddings are classified using an XGBoost model and evaluated through the KAN representation. The proposed ViT–KAN–XGBoost model achieved an accuracy of 85.11% on the C-NMC 2019 dataset, which is higher than the 83.22% obtained with the ViT–MLP–XGBoost model. This suggests that adding KAN-based representations to transformer features can improve classification performance and may be useful for leukemia screening tasks.
A Novel Content-Based Video Retrieval System Using MBH and BCHOA Optimized Multiple Features
Usage of multimedia data is increasing drastically every day, so being able to manage and effectively retrieve information based on its content has become important. A content-based video retrieval (CBVR) system retrieves video data based on visual content. This paper presents a CBVR system that uses visual descriptors, including Motion Boundary Histogram (MBH) temporal features, along with RGB codes and GLCM spatial features. By analysing the optical flow derivatives, MBH features are extracted from each frame to detect shot transitions and identify key frames that summarize the video content. From the keyframes, multiple features are extracted for dataset videos in the offline process and for the query video in the online process. To reduce redundancy and improve retrieval accuracy, feature selection is performed on the feature vector using various optimization algorithms, such as BPSO, BGWO, BWOA, and BCHOA. The performance of the system is tested on sample videos from the UCF50 dataset. The comparison between query and dataset videos is evaluated by computing cosine similarity. The retrieval performance and computational efficiency of the proposed CBVR framework are compared with existing methods by achieving a compression ratio of 0.9701, an F1 score of 0.859, a precision of 0.972 and a recall rate of 0.770.
Binary Particle Swarm Optimization (BPSO)
Binary Whale Optimization Algorithm (BWOA)
Binary Chimp Optimization Algorithm (BCHOA)
An Efficient Image Steganography Method Using Block-Based Indexing and LSB Substitution
A secure and efficient image steganography algorithm is proposed in this paper that uses block-wise indexing and LSB embedding. First, the cover image is divided into blocks of size 4 × 4. Blocks are classified as embedding blocks (E-Blocks) and index blocks (I-Blocks). Here, the 3-bit LSB substitution scheme is applied. The secret bits are matched with the least significant bits of the row pixels of the image, and the index values of those pixels are saved separately. In case of a mismatch, the secret bits are embedded in default pixels. The effectiveness of the proposed scheme is increased because the approach involves fewer changes to the cover image and does not use any deterministic extraction technique using indexing. The experimental results carried out by implementing the proposed scheme on various images of sizes ranging from 64 × 64 pixels to 512 × 512 pixels revealed that it produces high-quality images and efficient extraction. In the case of gray-scale images, the average PSNR, SSIM, and UIQI measures were found to be 55.64583 dB, 0.99869, and 0.99800, respectively, whereas for color images, their values turned out to be 49.19303 dB, 0.99209, and 0.99671. Overall, the presented technique provides an effective compromise between embedding capacity, invisibility, and robustness, thus being a good choice for secure image transmission applications.
A Hybrid Deep Learning Model Combining Tabular Transformers and Temporal Convolutional Network for Sepsis Prediction
Sepsis develops whenever our immune system's response to an infection becomes dysregulated, leading to extensive inflammation and organ damage. It is a severe immune system imbalance that, if ignored, can cause tissue damage, various organ failures, and death. Using multivariate clinical time-series data, a hybrid deep learning system combining a Temporal Convolutional Network (TCN) and a Tabular Transformer is developed for early prediction. The Sepsis Prediction Dataset is used in this work. The dataset consists of 40 clinical variables that have been gathered continuously in Intensive Care Unit (ICU) settings, which include vital signs, laboratory results, and demographic data. Two parallel branches are used for designing the proposed structure. The first branch, named the Tabular Transformer branch, learns the representations and interactions of variables through the handling of dynamic and categorical data. The Temporal Convolutional Network branch processes the sequential inputs by using dilated temporal convolution to detect long-range patterns within physiological signals. After concatenating both outputs and passing them into a fully connected layer, it makes predictions regarding the early phases of sepsis. With regard to its early predictions four to six hours before the clinical diagnoses, its sensitivity, specificity, accuracy, and F1-score were found to be 96.65%, 98.32%, 98.9%, and 96.3% respectively. These results indicate that the early detection of sepsis would improve as a result of integrating continuous patient data along with temporal patterns. Further validation of the developed model needs to be made on other clinical datasets.
MAAF-Net: A Modality-Adaptive Attention Fusion Network for Multimodal 3D MRI Brain Tumor Segmentation
Multimodal MRI offers a comprehensive, non-invasive assessment of structure, which improves the diagnostic accuracy of brain tumor segmentation (BTS). BTS faces major challenges due to tumor heterogeneity, data quality, modality-specific information and algorithm complexity. Many existing methods do not utilize the complementary information available in multimodal data, as they depend on early fusion strategies and neglect modality-specific features. To overcome these issues, a novel architecture termed MAAF-Net: Modality-Adaptive Attention Fusion Network for 3D BTS has been proposed in this study. The MAAF-Net model preserves the semantic information of each MRI modality through modality-specific encoders (MSEnc). The extracted features are integrated using a Modality Attention Module (MAM). The MAM learns the context-dependent importance of each modality and adaptively reweights modality features during fusion. This fusion technique enables the model to focus on clinically relevant information while discarding redundant or less informative features. In addition, multi-scale supervision is incorporated to improve gradient flow and training stability. The MAAF-Net model is trained and validated on the BraTS 2024 benchmark dataset using five-fold cross-validation. The MAAF-Net achieves Dice scores of 0.92 for Tumor Core (TC) and 0.91 for Enhancing Tumor (ET) with HD95 values of 3.7 mm and 3.5 mm, respectively. Additionally, compared with early and attention-based fusion methods, MAAF-Net improves Dice scores by up to 8% for ET. Experimental findings from the ablation study further validate the effectiveness of the proposed model.