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)
Copyright (c) 2026 Journal of Innovative Image Processing
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.
Multimodal Learning for Breast Cancer Biomarker Prediction Using Whole Slide Histopathology Images
Globally, breast cancer remains a significant health challenge that has a direct effect on women's cancer morbidity and mortality. The estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are important factors that help doctor to determine the best treatment for each woman. When using immunohistochemistry and genomic assays to look for markers, it is a relatively long and slow process that varies from individual to individual. The aim of this study is to develop a deep-learning framework to predict directly the ER, PR and HER2 status of H&E-stained histopathology images. The technique entails downsampling Level-1 slide images from the TCGA-BRCA cohort, followed by using a pre-trained ResNet50 architecture to extract histological features to enhance the accuracy of biomarker prediction. We train a multi-output classification model using XGBoost that adds a classifier chain. We use a mixture of clinical and genetic data as well as image features. This joint computational method shows promise in enhancing the accuracy of biomarker predictions and enabling doctors to customize breast cancer treatment for individual patients.
Image Cryptography Enhancement of the Arnold Cat Map Algorithm Using Long Short-Term Memory Generated Sequence
The recent massive shutdowns show that we need to guard against cyberspace as if it were a battlefield. Thus, one must consider the advancement of data encryption and decryption techniques. For enhancing the ACM algorithm, we propose in this study a dual-stream network with a generated sequence network based on Long Short-Term Memory (LSTM). The main purpose of the integration was to overcome the inherent drawbacks of predictability and non-periodic pixel reshuffling. Results show superior performance with the enhanced ACM generated via an LSTM, with higher entropy (5.67) and a lower correlation coefficient (0.006), compared with the original ACM, which has lower entropy (5.30) and a higher correlation coefficient (0.013). Additionally, the enhanced ACM exhibits a stronger, more substantial effect (130.52) than the original ACM (66.25), suggesting greater resistance to differential cryptanalysis. Therefore, the proposed enhancement of the ACM via LSTM-generated sequences improves security by increasing pixel unpredictability and randomness and by distorting image information.
Automatic Computer-Aided Diagnosis Tool for Glaucoma Detection Using U-Net and CNN on Retinal Fundus Images
Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blindness worldwide, where vision loss can be prevented only through timely diagnosis and intervention. Therefore, there is a requirement for the development of an automatic screening system to detect glaucoma at its preliminary stages. In this work, a fully automated computer-aided diagnosis (CAD) framework is proposed for early glaucoma detection using color retinal fundus images. The proposed method integrates image validation, region extraction, data balancing, data preprocessing, structural segmentation, and deep learning-based classification. The optic disc and optic cup are then segmented with a U-Net–based architecture, which allows calculation of important anatomic measures such as the vertical cup-to-disc ratio and neuroretinal rim parameters. These handcrafted features along with deep feature representations are used to train a custom convolutional neural network classifier to discriminate between glaucomatous and healthy eyes. We test the proposed system on publicly available benchmark datasets and show that it has strong robustness and generalization capacity. The achieved accuracy, sensitivity, specificity and AUC are 88.38%, 84.95%,81.43% and 93.24% respectively, in the validation dataset. The whole CAD system is applied with an easy-to-use clinical interface implemented with Streamlit to realize real-time inference and be easily integrated into the screening workflow. The proposed method is a cost-effective and reliable technique for glaucoma screening and has significant potential for mass-screening and tele-ophthalmology applications, especially in resource-limited environments.
Real-Time American Sign Language Recognition on Edge Devices Using a Multilayer Perceptron
The ultimate purpose of human existence is often considered to be finding happiness and satisfaction. People will live happily only when they communicate their feelings, emotions and opinions others. Furthermore, the, further right to freedom of expression is a fundamental right but people who suffer from hearing problems cannot communicate. This may lead to their isolation from societal activities. To address this issue, sign language came into existence, primarily to enable communication, education, employment and social inclusion. In this paper, an efficient methodology for American Sign Language (ASL) recognition using hand gestures is proposed. To find an efficient model, several experiments were conducted on ASL datasets which consisting of images, where each image represents signs for several words, alphabets, numbers, etc., Finally, the Multilayer Perceptron model with 2 hidden layers (1024,512) outperformed other models in terms of accuracy (99.3%) in static sign classification. These studies focused mainly on the recognition of legal words, as hearing people are unable to solve their legal issues because they cannot communicate effectively with their signs to the police or lawyers to raise their issues. In this methodology, the MediaPipe framework is used to extract landmarks from high quality sign-images, and pre-processing is done with normalization techniques, ensuring consistency in scale and orientation to generalize the model for various types of users in different environments. Experimental results demonstrated high classification accuracy, validated the model's performance and presented its potential for a range of practical applications in accessibility and human-computer interaction. A separate lightweight prototype was developed to evaluate optimal deployment models for real-time offline inference with quantized and non-quantized models on mobile hardware (CPU, GPU, and TPU) as additional work in this paper.
DSC-SwinNet: A Dual-Stage Transformer Framework for Reliable Brain Tumor Segmentation and Classification from Multi-Modal MRI
The earlier diagnosis of brain tumors is a critical challenge that influences treatment and facilitates prompt detection of the disease. Conventional MRI provides a structural and functional view of the tumors. On the other hand, recent deep learning-based algorithms, particularly single-stage convolutional neural network-based models, face challenges in providing the exact location of the tumor as well as in enhancing detection and classification accuracy. This is due to a lack of global-local integration of features, lack of spatial consistency, and low resistance to intensity variation, which are typical of clinical MRI scans. In order to address these gaps, the proposed research uses the DSC-SwinNet algorithm, which consists of a dual-stage transformer structure primarily utilized for tumor segmentation and classification. The first step employs a Swin Transformer-based encoder-decoder that uses window-based multi-head self-attention to simultaneously obtain local lesion features and long-range global contextual features of multi-modal MRI volumes. The next stage, known as Dual-stage Classification (DSC), is responsible for incorporating the ROI characteristics with conceptual representations of the tumor to identify the type of tumor. The proposed DSC-SwinNet has a Dice score of 0.934, an IoU of 0.891, an HD95 of 3.70 mm, achieving a classification accuracy of 97.8%, an F1-score of 97.9%, and an AUC of 0.99 on the BraTS multi-modal MRI data, demonstrating the potential of DSC-SwinNet as a clinically reliable brain tumor analyzer.
Robustness Analysis of Quaternion Fractional Moment Descriptors for Geometric Pattern Recognition Under Photometric Noise
Quaternion Fractional Moment (QFM) descriptors have been widely used for geometric pattern recognition because they can represent multi-channel data and have some degree of invariance. However, they have not been systematically tested for their robustness. It has been particularly unclear to what degree they are robust when the geometric pattern is corrupted by noise influenced by lighting variations, which are common in imaging modalities. A robustness analysis framework for QFM descriptors has been developed in photometric noise-controlled environments. The approach proposes to include bounded spatiotemporal perturbations in the feature extraction process to obtain several realizations of the descriptor for a single geometric pattern. A stability measure indicative of the degree to which a particular feature tends to spread out from the mean of the descriptor, in the context of feature dispersion, ensures that robustness is measured directly in feature space. The classification accuracy is compared to a deterministic QFM and the stability behaviors of the two are linked to that of the QFM. Numerous experiments are conducted on Moroccan-Andalusian geometric patterns with p4m and p6m symmetries. It is supplemented with additive Gaussian noise of varying intensities for photometric stability. As inferred from the results, the proposed approach is able to improve stability in the descriptors with noise and ensure stable performance in recognition accuracy.
Integrated Feature Learning and Decision Modeling for Land Use and Land Cover Analysis
Training the neural network using high-quality labeled data is a major challenge, as there are many grey areas in image classification. Hence, experts are available to label the data. Proposed in this research paper is a Heterogeneous Multi-Stream Deep Learning Framework. Its application of six advanced CNNs will remove such complications using their complementary inductive biases. An evaluation of two fusion paradigms was successfully achieved: a decision-level weighted average ensemble and a multi-stream CNN-SVM at both feature levels. The feature-level fusion method was found to be more discriminative than probabilistic averaging in testing across three different datasets: NWPU-RESISC45, UC Merced, and AID. This approach achieves the best results on all datasets, with our method achieving a highest F1-score of 97.24% on the NWPU-RESISC45 benchmark. The performance of the six-stream design, having 8192 features, was slightly affected, dropping to 97.15% because of the curse of dimensionality. The findings support the five-stream CNN-SVM as the best architecture since it easily strikes a balance between feature richness and the complexity of the classifier.
ResAtt-NASFPN: A Residual Attention Driven NAS-FPN Framework for Robust 3D Brain Tumor Segmentation
Multi-parametric MRI scans are used to accurately identify the morphological variability of glioblastoma sub-regions of the brain tumor. For this purpose, T1, T1CE, T2, and FLAIR modalities are used to identify essential information of about ET, TC and WT for treatment planning. Small ET regions struggle to be identified by traditional architecture because the area occupied by ET is less than 2% of brain volume, and even spatial resolution adoption is one of the challenges. The paper proposes an Enhanced Neural Architecture Search-based Feature Pyramid Network UNet3D (NAS-FPN-UNet3D) that incorporates autonomous operation selection inside multi-scale feature pyramids. In NAS-FPN-UNet3D, adaptive feature reconfiguration is managed by Convolutional Block Attention Module (CBAM). To maintain image quality and spatial accuracy, gradient stability is managed through residual blocks. NAS is one of the important components for effectively refining long-range information through five parallel operations: Standard Convolution, Dilated Convolution, Depth-wise Separable Convolution (with 94.7% parameter reduction), Asymmetric Convolution and identify connection. Operations are automatically selected based on the learnable parameters; for contextual modelling, dilated convolutions are preferred at coarse levels, while for efficiency at finer levels, depth-wise operations are employed. When evaluated on the BraTS2020 dataset, the proposed model uses parameters and yields Dice scores of 0.8406 ± 0.05 (ET), 0.868 ± 0.06 (TC), and 0.9157 ± 0.03 (WT) with corresponding Hausdorff distances of 4.57mm ± 0.05 mm, 6.75 mm ± 0.04 mm, and 6.85 mm ± 0.02mm, respectively. The proposed model requires 34% fewer parameter than fixed-convolution baselines while achieving greater accuracy.
Protozoa Optimized Deep Perceptual Enhancement Network for Lung CT Imaging
The precise feature extraction of small details from images of the human body is considered a challenge since fine details like small nodules and delicate vasculature are hard to see while the computed tomography scan is being conducted due to low contrast. Although various contrast enhancement methods have been introduced for better visibility of the images, they generally introduce more noise and edge artifacts. In this paper, a self-supervised Deep Perceptual Enhancement Network with an Artificial Protozoa Optimizer named APO-DPENet is proposed for optimizing the parameters of the image effectively. The results comparing the proposed method with other methods show a PSNR of 25.75308 dB, an SSIM of 0.95894, a CEI of 0.91385, and an EPI of 0.98960 units. The results indicate better contrast retention and edge preservation of the proposed method, along with improved noise removal capabilities. This suggests that the proposed method is robust enough for analysis.
SmartCovNet: An Intelligent ResNet-50 and Novel Neural Classifier Structure for COVID-19 Discovery Via CT Imageries
The COVID-19 pandemic has necessitated immediate attention towards the development of novel structures. The RT-PCR test is the widely used technique for diagnosis; however, it poses disadvantages such as longer delays and inconsistency in test results. We present a SmartCovNet framework to distinguish COVID and non-COVID cases using CT scan images. An integration of a pretrained ResNet-50 architecture with a novel neural network classifier is employed. The lung regions are highlighted by employing adaptive histogram equalization with contrast restriction. After extracting the features from ResNet-50, we pass them to the novel neural network classifier. SmartCovNet has achieved a classification accuracy of 99.53%, along with high specificity, sensitivity, and F1 score. The performance metrics indicate the effectiveness of SmartCovNet in the diagnosis of COVID infection.
DASS-Net: A Multi-Branch Aesthetic–Spectral–Semantic Deep Learning Model for Food Spoilage Detection in Hospitality Operations
Food freshness and food safety in the hospitality industry are, to date, mainly dependent on visual observation, where human visual interpretation does not always successfully identify the less obvious signs of early food spoilage. In this work, we introduce DASS-Net, a novel hybrid deep network with a trilinear structure and three separate task-focused branches: (1) the RGB aesthetic branch, (2) the LAB spectral branch, and (3) the Vision Transformer ViT-Small semantic branch, used together for simultaneous analysis of food image-based texture degradation, color deviation, and overall food image-related semantic dependencies on a global scale. Each of the network's task-focused branches projects its outputs to a unitary latent space and further refines them with multi-head attention and adaptive joint processing with a learnable gated fusion module. To train DASS-Net, a dual-objective training approach with class-weighted and smoothed Cross-Entropy loss and Contrastive Learning with MixUp and CutMix data augmentations is used. Eight-class classification experiments on fresh and spoiled food samples of bread, dairy products, fruits, and vegetables are conducted to evaluate the performance of DASS-Net, providing a validation accuracy of 94.33%, which demonstrates a 23.18% and 25.83% relative improvement over ResNet-18 with LAB and RGB visual channels, respectively. Additionally, the classification model yielded a minimum misclassification rate of 13.30% for spoiled items identified as fresh, which clearly marks an important characteristic related to safety issues. The complementarity between the spectral and aesthetic features has also been proven by the Grad-CAM visualizations of the dual-branch network, where the most confusion occurs in the case of dairy products with negligible surface decay, as revealed by the analysis of failure cases. The results explicitly confirm that the developed DASS-Net delivers a safe, efficient, and scalable vision solution applicable to kitchen intelligence and hospitality control scenarios.