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.
Depthwise Residual Transformer for Retinal Vascular Enhancement using Fundus Images
The high-quality visual information in fundus images enables highly accurate clinical judgment for the diagnosis of eye diseases. The image quality subsequently suffers due to factors such as refractive media excretions and inconsistent patient cooperation. To avoid these conditions, we propose a depthwise residual (DWR) transformer-based enhancement network for distorted fundus images. The proposed framework contains four blocks: the DWR block, the encoder block, the transformer block, and the decoder block. The DWR block is designed to improve model generalization over noisy inputs by applying channel-wise filtering. The encoder block has convolutional filters to capture fine-grained local features from the input images. The transformer block effectively captures global dependencies from the encoder blocks. The decoder block works for spatial resolution reconstruction while concatenating contextual features from the encoder with fine-grained information for accurate localization. The proposed model performance is evaluated through both subjective and objective analysis on open--source datasets such as DRIVE, STARE, CHASE_DB1, HRF, and MESSIDOR. Our proposed method achieves an excellent SSIM of 0.901 with the DRIVE dataset and PSNR of 31.596 with the MESSIDOR dataset, surpassing state–of–the–art methods across all datasets.
A Hybrid Optimization Framework for Early Alzheimer’s Disease Detection Using MDKM- Segmented MRI and DenseNet169
One of the common neurodegenerative disorders is called Alzheimer’s disease (AD), which gradually reduces human memory, thinking ability, and cognition. Deep learning (DL) approaches play a vital role in early AD prediction because they do not require manual feature learning like traditional machine learning (ML) techniques. However, further improvement is still possible in terms of detection accuracy by reducing training time and minimizing overfitting. This research study develops a DL technique for early AD prediction and includes a pre-trained convolutional neural network (CNN) based feature extraction. The proposed technique consists of three phases: (1) preprocessing, (2) segmentation, and (3) classification. Initially, the system collects the input MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Then, contrast-limited adaptive histogram equalization (CLAHE) is employed to enhance the contrast of the gathered data from the dataset. The model then employs the Mahalanobis distance-based k-means (MDKM) algorithm to segment the skull portions from the input images. Finally, the system utilizes the osprey-optimized densely connected network 169 (O2DenseNet169) to classify AD, with hyperparameters optimally selected using the osprey optimization algorithm (OOA). The proposed technique achieves 99.44% accuracy, demonstrating the effectiveness of O2DenseNet169 in automatically learning biomarkers from images to classify AD.
Self-Supervised Semantic Prior Generative Adversarial Network for Heritage Image Restoration
The heritage images are subjected to large and irregular missing regions due to long-term degradation. In order to repair the gaps, a filling process is used which creates visible seams. High computational expense and prolonged inference time are involved in diffusion and transformer models. A framework for restoration based on GAN is built to handle the irregular missing regions while preserving delicate brush strokes and structural lines with faster speed. The proposed framework makes use of the Adaptive Semantic Prior Injection (ASPI), Topology-Aware Semantic Refinement (TASR) and Entropy-Guided Self-Supervision (EGSS). ASPI contextualizes guidance while TASR provides line and edge continuity, and EGSS emphasizes uncertain regions during training. SSPL learns semantics from masked images with no labels and is used to aid the performance of irregular restoration. The Kaggle Damaged Paintings dataset is used to train the model with an image resolution of 256×256 for 50 epochs. Masked WikiArt images are utilized for self-supervised prior learning. The Indian Heritage Monuments pictures are validated for model performance. The model recorded a Peak Signal-to-Noise Ratio (PSNR) of 32.6 dB, a Structural Similarity Index Measure (SSIM) of 0.92, and a Fréchet Inception Distance (FID) of 18.7. According to the results, diffusion and transformer-based baselines are consistently outperformed. Addditionally, more texture continuity is gained when compared to exemplar based inpainting and Conditional GAN (cGAN) using partial convolution-based models. The ablation study shows that TASR mainly promotes structural fidelity while ASPI and EGSS provide additional benefits. The architecture remains efficient with 34.8 million parameters, 108.5 Giga Floating Point Operations (GFLOPs), and an inference time of 0.118 seconds per image Despite the improvements, the model remains unable to manage thin cracks and the edges of the restored region can hardly be seen. Additionally, significant omissions may not completely correspond to the original in the final restoration. Future studies will focus on training at greater resolutions and applying diffusion priors appropriately.
Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States using Multimodal Biosensor Data
Subjects exhibiting neurological, developmental, and behavioral disorders can manifest stress, agitation, and emotional deregulation. The prolonged emotional deregulation increases the development of depression, anxiety disorders and in extreme cases, this persistent deregulation contributes to onset of psychiatric symptoms. This study proposes a novel approach for the detection of psychiatric risk and abnormal behavioral states using multimodal biosensors integration namely Pulse, Electromyography (EMG), and Galvanic Skin Response (GSR) sensors. This experiment was conducted in a skill-training centre for Endosulfan victims aged 13±2 years with the necessary care and consent. Significant differences in electro-dermal signals such as GSR, pulse, and EMG were observed among 66 subjects, with multiple trials conducted for each subject, resulting in a dataset comprising 793 healthy and 750 high risk samples, respectively. Data acquisition was performed by integrating sensors onto the Arduino UNO microcontroller and the Cool-Term software tool. In total, fifteen nonlinear transform domain features were extracted. The confidence intervals of the features were verified using the t-test (p<0.05). A classification model was created using machine learning algorithms, namely Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost, and Neural Networks. Among these, Random Forest, XGBoost, and Neural Networks achieved a sustainable accuracy of 97.92%, while the ensemble model achieved an accuracy of 98.32%. This approach of biosensor monitoring helps to consistently predict the behavioral states of differently abled individuals and supports healthcare providers in the early identification of behavioral and physiological markers indicative of psychiatric risks, contributing to a sustainable mental health ecosystem.
A Hybrid Multitasking Cascaded CNN leveraging Xception Architecture for Enhanced Face Recognition
Face recognition technology plays a significant role in surveillance systems, authentication systems, and systems that allow people and computers to communicate with each other. The main aim of face recognition technology is to identify individuals based on their facial features. It is crucial to analyze facial features accurately in today's world, where numerous smart devices and surveillance systems are installed globally. Deep learning algorithms such as CNN, VGG16, and ResNet have shown promising results in improving the accuracy of face recognition technology, but the results are not sufficient in open environments. This paper proposes a new face recognition system that utilizes deep learning concepts. The proposed system aims to perform tasks such as detecting faces, locating landmarks, and recognizing faces. It employs depth-wise separable convolutional networks to reduce the number of parameters and optimize the features. The system is rigorously tested using various datasets, including LFW, CelebA, and CASIA-WebFace, and demonstrates exceptional performance by achieving 98.3% accuracy in face recognition. Additionally, the proposed system achieved 4.8% higher accuracy and 32% lower latency compared to existing face recognition systems.
FR-GAN: A Fusion Refine with Dual Attention Architecture for Denoising of Dental Panoramic X-ray Images
Denoising dental panoramic X-ray images has become a major concern in medical imaging and computer vision, particularly when the Gaussian noise is high. To denoise the noisy images while preserving image features, we introduce a fusion-refine generative adversarial network (FR-GAN). The FR-GAN comprises a generator, a dual-attention U-Net, and a ResNet enhanced discriminator. The generator takes noisy images as input and produces coarse representations for initial reconstruction. Then, these coarse images are used as a preliminary denoised image, which is again refined to restore structural features for image clarity. In addition, we incorporate the U-Net with a dual-attention mechanism linking the generator and discriminator, which enables the refinement of coarse features into smooth features to create meaningful representations. Meanwhile, the U-Net refiner incorporates progressive feature fusion attention and low-rank local window attention to complement multi-scale feature extraction and local texture refinement. Lastly, the ResNet block-enhanced discriminator implements perceptual realism by differentiating genuine and reconstructed images and directing the network to yield high-quality images. In our experiment, 100 dental images collected from Al-Badar Dental College and Hospital, Kalaburagi, were subjected to Gaussian noise at 20 dB and 40 dB using ImageJ software. To evaluate the effectiveness of the proposed approach, experiments were conducted with Gaussian noise at 20 dB and 40 dB. The experimental results show that the proposed method is superior to other state-of-the-art approaches for Gaussian noise of 20 dB and 40 dB. It achieves a PSNR of 31.705, SSIM of 0.826, MSE of 0.0007, and MAE of 0.0195 at 40 dB, and a PSNR of 32.897, SSIM of 0.861, MSE of 0.0007, and MAE of 0.0184 at 20 dB. The framework provides effective high-quality denoising with clean edges, clear textures, and few artifacts, and is applicable to precision-critical tasks, such as medical and scientific imaging.
Adversarial Temporal Modeling for Seizure Classification Using WGAN-GP-LSTM Networks
Detecting an epileptic seizure with the help of electroencephalogram (EEG) signals is difficult due to the absence of classified data or complexity. The conventional GAN-based data augmentation methods suffer from instability and inadequate learning of dependencies, which limits their efficiency despite increasing the diversity of data. An LSTM method is proposed for binary seizure classification with the WGAN-GP-LSTM scheme to overcome the previous issues. The generator and the discriminator of the system proposed in this paper use LSTM units, which differentiate it from other previously proposed systems. The system can produce acceptable EEG signals. This also helps in the correlations being learned over time for seizure classification. Through this system, EEG datasets that contain signals with and without supplements are publicly available. Results indicate that using synthetic data augmentation will enhance the WGAN-GP-LSTM model’s performance. Consequently, this model outperforms WGAN-GP and other prior works on the overall score, which are 0.96, 0.875, 0.82, and 0.923, respectively, for overall accuracy, sensitivity, specificity, and F1 score. WGAN-GP, with an FID score of 3.07 is poorer than WGAN-GP with a score of 1.42. The WGAN-GP-LSTM model displaces adversarial sequence generation and classification, helping to overcome challenges of data scarcity and the temporal dependence of EEG signals for seizure detection. A different approach to computer -assistance in neurodiagnostics has been introduced.
Hierarchical Attention Modeling for Handwritten Prescription Recognition: A Swin Transformer-Based Solution
Many doctors’ prescriptions are handwritten and often unreadable by the chemist who sells the medicine. Patients have been put in jeopardy as a result. A deep learning architecture based on the Swin Transformer for handwritten medical prescription identification has been proposed in the research paper. The proposed architecture has both a local and a global aspect. To identify character features, the local part uses handwriting features and contextual patterns through a hierarchical attention mechanism. The recommended framework is very easy to comprehend for both the handwriting style and the distortion. The BD dataset has 4,680 labeled images showing 78 different words associated with medicine. Handwritten prescriptions from doctors are included. The dataset also allows for the creation of a comprehensive preprocessing model that scales, normalizes, and applies sophisticated data augmentations that simulate real-world conditions. The test accuracy obtained was 89.0% with macro and weighted F1 scores of 0.88, performing better than some existing techniques involving CNN and CRNN. In addition, the results of the confusion matrix validate the model's ability to detect similar drug names. To demonstrate the efficiency of the model in segregating illegible handwriting from well-written handwriting, a qualitative study of the model’s performance was undertaken. The researchers showed that using the transformer model, they could effectively digitize handwritten prescriptions, which can successfully curb the errors associated with the selling of medicine. The study on the semantic understanding of the obtained results through the application of optical character recognition and language models via multi-modal fusion is a future direction for research.
Hybrid InceptionV3-LSTM for Asthma Detection on Chest X-Rays
Asthma is commonly undiagnosed in areas without access to lung-function tests, whereas CXR is more commonly used to diagnose conditions of the lungs. The prototype is a pipeline based on InceptionV3LSTM that produces a 1024D projection from ImageNet-initialized InceptionV3 spatial features and global average pooling. A sigmoid classifier and LSTM (64) are applied to this, which has been resized into a short sequence of 1024D projections. This model is trained using a two-stage procedure that includes the previously mentioned selective fine-tuning with early stopping and frozen transfer learning in the first stage, in addition to conventional preprocessing and conservative augmentation. The dataset, comprising 4295 chest X-ray images, achieved a successful classification accuracy of 87.12%, precision of 85.64%, recall of 81.73%, F1 of 83.64%, and ROC-AUC of 0.94. This suggests that if we only concentrate on optimizing our thresholds, we can obtain higher specificity. The straightforward DenseNet-121 and ResNet-50 deep learning models can be used to illustrate the use of the sequence head. This is followed by external validation, probability calibration, and saliency audits. Referral and decision support systems are also applicable.
Detection and Segmentation of Early Gastric Cancer using Graph Learning Based Improved U-Net Architecture
It is difficult to identify Early Gastric Cancer (EGC) through common gastroscopy because of its sharpness and low contrast with the mucous membrane. To enhance EGC detection and segmentation, a GLIU-NET is proposed, using a graph learning enhanced U-Net model, which adopts a bi-directional feature extraction and fusion module. It adopts a composite loss function to promote lesion segmentation. The experimental results demonstrate that the GLIU-NET performs better than conventional models. The model reaches a Dice of 0.89, IoU of 0.83, and F1-score of 0.88. The ablation study verifies the effectiveness of the graph spatial modelling and feature fusion. Analysis of the research results showed a marked improvement in segmentation accuracy and boundary accuracy with GLIU-NET. Medical professionals showed positive feedback about the lesion boundary clarity. This is a positive indication of the clinical practicability of the GLIU-NET for early gastric cancer detection.
Agentic-TabDeCap: An Agentic AI Enhanced Multi-Stage Framework for Depression Detection and Support
As the global burden of depression continues to rise, there is an urgent need for efficient and accurate methods for early detection and severity assessment. Agentic-TabDeCap is a novel multi-stage framework that detects depression and estimates its severity. In the first stage, all instances are processed by a TabNet-based classifier, which determines whether an individual is depressed or not and assigns corresponding confidence scores for each case. The DeBERTa-v3 and Capsule Network-based model assesses everyone in the second phase, predicting four categories: no depression, mild, moderate, and severe, thereby allowing the system to correct any misclassifications made in the first stage. To avoid error propagation between stages, a Confidence-Guided Attention-Based Meta-Classifier is introduced as the third stage, merging probabilistic outputs from the two former models to produce the final, robust prediction. The model developed recorded an astonishing 98.21% accuracy, 98.83% precision, 97.47% recall, 97.15% F1-score, and 98.34% AUC. For real-time and empathetic mental health support, an Agentic AI system using Retrieval-Augmented Generation (RAG) is integrated, with a knowledge base embedded via all-MiniLM-L6-v2 and indexed with FAISS, while TinyLlama generates context-aware responses. A prototype web interface has been integrated, demonstrating the feasibility and practical applicability of the complete system, including both depression assessment and human-like supportive responses.
Confidence-Guided Attention Meta-Classifier
Multimodal Stroke Lesion Segmentation with Hybrid Attention and Transformer Architecture
Accurate and early stroke lesion segmentation is necessary for better results for patients and successful treatment. A novel deep learning model using a multi-modality architecture that combines spatial, channel, and temporal information from perfusion MRI and CT images is proposed in this work for the deep segmentation of stroke lesions. The model addresses cross-domain generalization by integrating dynamic perfusion information from the ISLES 2022 dataset, such as Diffusion Weighted Imaging (DWI) and CT Perfusion (CTP) images alongside high-resolution anatomical data obtained from the ATLAS v2.0 dataset. We enhance the proposed approach by adding a hybrid spatial channel temporal attention transformer block to a UNet encoder-decoder architecture. Multi-scale features that are patch-embedded and enhanced with positional encoding are extracted by the encoder. To increase sensitivity to anatomical features and corresponding pathological changes, the attention module integrates temporal encoding of perfusion parameters like Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV), Mean Transit Time (MTT), and Time-To-Maximum (Tmax) with joint modeling of spatial and channel dependencies. The experimental results contribute to reliable clinical application by exhibiting increased accuracy and robustness over traditional UNet and unimodal transformer models.
A Novel Optimized Hybrid ResNextTransform for Dental Diseases Classification with Crested Porcupine Optimizer
Artificial intelligence is revolutionizing healthcare by assisting medical professionals with disease prediction, diagnostic procedures, and efficient treatment planning. Deep learning has emerged as a revolutionary technology in contemporary dentistry, which can handle dental images efficiently and produce accurate diagnostic results without human intervention. Traditional deep learning models lack generalizability, accuracy, and interpretability of local features and global interaction. Here, the hybrid ResNextTransform model with grouped convolution is developed to classify various dental disorders, and the metaheuristic Crested Porcupine optimizer (CPO) is employed to optimize the hyperparameters. The proposed model is used to classify the dental images into caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia. The ResNextTransform model achieves 95.9% accuracy, 95.4% sensitivity, 95.7% precision, 95.2% AUC, 95.5% specificity, and 95.1% F1-Score. The performance of the model significantly improves with CPO optimization and produces 97.3% accuracy, 97.4% sensitivity, 97.2% precision, 97.2% AUC, 96.9% specificity and 97.1% F1-Score.
Benchmarking Lightweight Convolution Neural Networks for Children’s Arabic Handwriting
The children cannot recognize the Arabic script handwriting because the recognizer is intolerant of high inter-individual variations. The issue is further compounded by other problems such as irregular strokes, interrupted forms and inconsistent aspect ratios. The study explores which CNN architectures are most suitable for robustly recognizing black-and-white Arabic letters and digits drawn from kids, aged 5-10. The dataset comprises 570 000 characters images. The six well known CNN architectures are LeNet 5, AlexNet VGG16, GoogLeNet, DenseNet, and ResNet50. In order for the experiments to be reproducible and easily verifiable, we used a supercomputer for all models training and tests. The ResNet50 model was shown to perform best of all models with a validation accuracy of 99.86%, a global F1 score of 99.89%, validation loss of 6% and 0.96 GFLOPS. Along with benchmarking, the proposed work provides optimized lightweight CNNs for 64×64 grayscale images of children’s handwritten Arabic characters. The suggested model achieves a recognition accuracy of 98.3% at a cost 41% lower than VGG16, while drastically reducing the number of parameters. According to the study, modeling various handwritten text types can benefit from residual learning. LeNet-5 and other lightweight models have demonstrated good performance with less processing power and can be applied to embedded systems. The results indicate that children's handwriting can be automatically analyzed using the improved CNNs. Additionally, they demonstrate the applicability of these CNNs in digital assessment systems, educational technologies, and multilingual writing processing. The study offers an AI method for interpretability and robustness and lays the foundation for hybrid CNN-Transformer models.
Instance Segmentation for Local Rice Seed Germination Evaluation using Deep Learning
This study combines sophisticated deep learning technology to solve the manual procedure of rice seed germination evaluation which is time-consuming and error-prone. This paper examines the architectural behavior of CNN architectures in detecting, segmenting and classifying local rice seeds into normal, abnormal, fresh ungerminated and dead seeds. This is done using Convolutional Neural Network (CNN) architectures such as You Only Look Once (YOLO)v8s-Seg and YOLOv9c-Seg. A high-end smartphone was used to capture the image in natural light, and polygon annotation was used to label an image of seeds that covers individual, partially occluded and overlapping seeds in a single image. A post-processing technique called Segment Anything Model (SAM) was adopted to improve the mask borders of the rice seed objects for accuracy. The outcome illustrates that both models function well with a mean Average Precision (mAP@50) of more than 90%. However, there are misclassification cases identifying rice seeds between dead and fresh ungerminated, and then abnormal and normal. The YOLOv8s-Seg was shown to be more computationally efficient for mobile deployment where it has 40.2 Giga Floating Point Operations Per Second (GFLOPs). Meanwhile, YOLOv9c-Seg was found to be better for feature extraction and fast convergence. However, YOLOv9c-Seg was computationally complex for mobile edge deployment. The research reveals that the incorporation of automated seed quality assessments from an artificial intelligence (AI) framework to empower the process can drastically ramp up agricultural productivity and sustain economic growth.