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.
Copyright (c) 2026 Journal of Innovative Image Processing
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.
Radio thermal Dual-Imaging Fusion Network for Deep Feature-Driven Breast Cancer Detection
Breast cancer is one of the major problems affecting the breast that is very commonly detected in females, and it requires efficient and precise diagnosis for enhanced stages of survival. To achieve efficient, precise, and contactless diagnostic processes, it is proposed that the Radio Thermal Dual-Imaging Fusion Framework be utilized in combination with the structural information obtained from mammogram images, along with thermal information obtained from infrared images of thermograms of the breasts. This paper proposes a conceptual design that makes use of an available set of mammogram images taken from digital image databases, such as The Mammographic Image Analysis Society Database (MIAS), standard sets of samples from the curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), as well as samples from anonymized patients in recent routine clinical screenings, similar to recent diagnostic studies that utilized mammography. The samples for thermal images were obtained from infrared images of thermograms of breasts, which are still very much in use in today’s breast cancer diagnostic research. The proposed conceptual design for diagnosis will employ the Super-Fast Recurrent Convolutional Neural Network (SFRCNN) architecture, which will be a ResNet-50 based architecture for the extraction of thermal images and standard images, as mentioned above. The preprocessing for sampling the modalities will be done using grayscale normalization of the standard mammogram images as well as standard thermal mapping for the infrared images. From the results of the proposed conceptual design, it has been identified that the proposed dual-imaging modality accuracy of 84.75% will be obtained by using the standard mammogram images and the standard infrared images, representing a significant improvement over standard image processing, as an improvement in accuracy of 3% is expected from the proposed standard approaches within the benchmark design for the Convolutional Neural Network model due to the proposed dual-imaging modality technique, along with a proposed diagnosis accuracy for assessing a sensitivity of 98.04% by the dual-imaging modalities of the standard infrared images.
Pneumonia Detection Enhanced by Conditional Generative Adversarial Networks cGAN Addressing Class Imbalance with High Quality Synthetic Data
Pneumonia has recently reported the highest number of deaths in the entire world. Consequently, diagnostic procedures ought to be precise. Conversely, the problem of class imbalance is a universal issue in medical image classification that may result in biased models that do not perform well in underrepresented classes. The issue of class disproportion is successfully addressed in the work under consideration, as a cGAN generates high-quality synthetic images of the minor classes, allowing for the creation of a balanced dataset that, in turn, leads to high sensitivity of the model and consequently improves its overall performance. This paper presents a new Conditional Generative Adversarial Network (cGAN) architecture to improve the detection of pneumonia in the provided chest X-rays. To mitigate the issue of data imbalance in the dataset, this work suggests a conditional GAN-based augmentation process for synthetic X-ray images by producing clinically viable and label-coherent synthetic X-ray images. The framework also includes validation achieved through the application of either SSIM/FID or balanced training, which improves the accuracy of pneumonia detection and leads to diagnostic conclusions unlike those of existing methods. Current approaches are less successful compared to this study, as the system achieves 96.5% accuracy, 95.2% sensitivity, and 96.1% specificity, with a high F1 score. The suggested framework has a good performance scale, making it applicable in medical applications. This paper demonstrates the capacity of cGAN to develop pneumonia diagnosis machines that are feasible and user-friendly in healthcare institutions.
Conditional Generative Adversarial Networks (cGAN)
Detection of Multi-class Skin Cancer using Stochastic Gradient Descent Augmentation Model and Activation Mapping
Early detection of skin abnormalities is essential in lowering fatality rates and ensuring timely treatment. However, currently available techniques encounter limitations such as small datasets, variability within the same lesion classes, imbalanced complex data and minor visual differences. These challenges make it difficult for conventional machine learning to accurately classify multi-class skin cancer abnormalities. To overcome these issues, this paper presents a computational model for the efficient identification of skin abnormalities using an applied deep learning framework. In this study, a Dense Convolutional Networks (DenseNets) model with Spatial Pyramid Pooling (SPP) and active learning is applied for data enrichment and the identification of eight classes of dermoscopic skin cancer images, which are extracted from the International Skin Imaging Collaboration (ISIC) Challenge Datasets 2016 and 2018, respectively. In the proposed system, a data augmentation technique is realized by subjecting the features to the Stochastic Gradient Descent with Warm Restarts (SGDR) model, which reduces the overfitting problem and selects effective model parameters. The performance achieves a recall, precision, and F1-score of 96.2%, 97.8%, and 97.0%, respectively. In addition, the application of the Gradient-weighted Class Activation Mapping (GRAD-CAM) image visualization method guarantees model transparency. This technique supports medical experts in the efficient and early-stage detection of cancerous images.
Dense Convolutional Networks (DenseNets)
An Optimized Key Frame Extraction Technique for Content-Based Video Retrieval Using Hybrid PSO-BOA
Extraction of key frames is an essential and significant stage in any video analysis application, aimed at describing the video content precisely by removing the redundancy. An optimized key frame extraction technique based on the Binary Butterfly Optimization Algorithm (BBOA) is introduced. Here, the extraction of Color Coherent Vector (CCV) features from each frame of the video and the application of the BBOA algorithm for minimising redundancy and maximising diversity between the frames is carried out. The process is applied iteratively until the precise number of frames is selected. Further, the system is extended by proposing a Content-Based Video Retrieval (CBVR) system using the selected key frames, extracting multiple features from Grey Level Run Length Matrix (GLRLM) texture features and Dual Tree- Complex Wavelet Transform (DT-CWT) shape descriptors along with CCV features. Due to the multiple features, the feature vector size is huge, so to reduce its dimension, a hybrid Binary Particle Swarm Optimization-Butterfly Optimization Algorithm (PSO-BOA) feature selection method is applied. The experiment was conducted on the UCF101 dataset, and our proposed system outperformed with a compression rate, precision, recall rate, F1 score, and FRR of 0.985, 0.913, 0.78, 0.836, and 0.965, respectively, demonstrating the effectiveness of using the hybrid Optimization algorithm in improving the efficiency of the CBVR system.
Binary Butterfly Optimization Algorithm (BBOA)
Particle Swarm Optimization-Butterfly Optimization Algorithm (PSO-BOA)
Dual Tree- Complex Wavelet Transform (DT-CWT)
Multimodal Lie Detection Using Linguistic and Visual Cues: A Fusion of NLP and Facial Micro-Feature Analysis
The research integrates Natural Language Processing (NLP) and facial micro-expressions recognition methods for analyzing deceptive behavior. Lie behavior analysis is enhanced by the incorporation of both verbal and non-verbal communication in the assessment as subtle non-verbal cues are hard to detect during scrutiny. Different machine learning algorithms were evaluated based on their ability to detect lies in this study. Several classic models like Nearest Neighbors, Linear SVM, Decision Tree, Random Forest and Extra Trees Classifier were tested using the Real-Life Deception Detection and Own Dataset student viva scenario data. Various accuracies were generated by different traditional ML models until researchers developed a lightweight Convolutional Neural Network (CNN) model designed to efficiently detect deception. The lite-CNN model achieved a successful 96% accuracy in both tests on the dataset. The lite-CNN model identifies deceptions through its high performance by combining verbal speech and facial behavioral patterns. It has been found that deception detection is successful when using NLP with facial expressions providing reasonable solutions in the fields of security, psychology, and human-computer interaction. The proposed lightweight CNN model is a proven solution compared to traditional models, as it is effective yet consumes fewer computing resources.
DeFakeNet: A ResNet50V2-Based Deep Learning Model for Deepfake Detection and Classification
The spread of deepfake content has created distrust, misconception, and fraud all around the world. Swapping faces between individuals seamlessly or generating AI-created fake faces has become easier than ever with AI tools like DALL-E, Midjourney, ChatGPT, and Google Gemini. AI generated obscene and malicious content has become progressively prevalent and widely circulated on social media due to the misuse of generative adversarial techniques. To prevent AI generated fake images from causing harm to the identity and social integrity of a person or community, this research presents a deep learning model called ‘DeFakeNet’ based on the advanced ResNet50V2 CNN architecture, designed to detect and classify whether a person’s face is real or fake. While past research has relied extensively on pre-trained models and limited dataset, DeFakeNet was trained on a custom-developed dataset titled ‘Real vs Fake Faces Balanced Dataset with Multiple Dataset Splits’, a mixed dataset comprising 10,000 high quality balanced real and fake face images. Upon testing with unseen data, the proposed model obtained 91.95% test accuracy and an AUC score of 97.64%, setting new records in this field. A critical scrutiny of all the diverse evaluation metrics, ROC and Precision-Recall Curves is presented in this paper, which is rarely discussed thoroughly in previous research. Additionally, the model performance comparison with current methods shows robust real world reliability and application toward the detection of evolving deepfakes.
Quantification of Carotid Atherosclerosis in MRI Images using Hybrid Transformer Model
The accurate evaluation of carotid atherosclerosis by MRI imaging is the most important factor in the assessment and management of stroke risks. Plaques can be quantified and delineated manually but this is labour-intensive and subject to error. The article describes an open-source software for the automation of carotid plaque segmentation and classification, following a hybrid approach involving the use of Med Transformer to generate high-resolution volumetric segmentation and Swin Transformer for feature-based classification. This method is more accurate, reproducible, and provides efficient carotid plaque delineation and quantification. Med Transformer attained high segmentation accuracy, with average Dice scores of 0.89 in lumen and vessel wall and 0.84 in plaque regions. Swin Transformer revealed strong performance regarding plaque type classification: the overall classification accuracy attained 91.41% and the area under the Receiver Operating Characteristic curve (AUC) was 0.9571. By fusing the results from both systems, segmentation and classification of carotid plaques could be performed under a variety of conditions and subjects with a volumetric error of less than 8%. These findings provide evidence that transformer-based systems are effective and accurate in analyzing carotid plaque in a fully automated manner, which can then be employed in scalable longitudinal studies to improve the accuracy of cerebrovascular risk assessment. The software pipeline simplifies big-data image analysis with objective and reproducible quantification. Models and scripts that are modular and developed can be integrated into clinical and research environments for further fine-tuning.
Lungs And Nodules Segmentation from Computed Tomography Scans Using Auxiliary U-Net
Due to the increasing prevalence of lung cancer and the urgent need for artificial intelligence technologies to support medical fields, there is an immediate demand for highly accurate and dependable automated diagnostic models for the segmentation of chest CT scans. Accurate segmentation will reduce clinicians' manual labor and improve diagnostic results. This paper proposes a new segmentation model, which combines an improved U-Net architecture with a squeeze-and-excitation mechanism. The model can utilize the loss in a multilevel fashion, thanks to the auxiliary classification at the bottleneck layer. Furthermore, the squeeze-and-excitation block focuses on the key characteristics extracted from convolutional layers. Experiments on the LUNA16 dataset were carried out to train and evaluate the model. The proposed approach outperformed many other segmentation methods and was very effective in defining the shape of lung nodules more precisely. On the LUNA16 dataset, it achieved 98.42% DSC and 98.56% accuracy. It has been proven that the segmentation of lung nodules in chest CT scans using an auxiliary U-Net architecture together with a squeeze-and-excitation mechanism is highly efficient and cost-effective. High accuracy and Dice coefficient against the most advanced segmentation models demonstrate the possibility of clinical usage and provide accurate boundary detection, which is crucial for an accurate diagnosis and treatment. The proposed contraction and stimulation mechanism with an auxiliary U-Net architecture has achieved very effective yet inexpensive lung nodule segmentation on chest CT scans, compared to state-of-the-art segmentation models. Accurate identification of the boundaries is of paramount importance for diagnosis and treatment planning, and the high Dice coefficient and accuracy support its use in clinical settings.
Detection of Voice and Lung Pathological Signal Using Acoustic Spectrogram Transformers
In the medical field, identifying various pathological conditions poses a crucial challenge because it requires an invasive and contact-based data extraction technique. Therefore, non-invasive and non-contact forms of vital data, such as speech signals, can be used to identify various pathological conditions. Speech signals have distinguishing phonetic characteristics that change when a pathological condition occurs in the human body. By using these changes, various pathological signals can be classified by training machine learning and deep learning models with the acoustic features of speech signals. This work proposes the acoustic spectrogram transformer, where all the layers in the transformer are trained using acoustic characteristics extracted from the speech signals of voice and lung disease patients. Mel-frequency cepstral coefficients (MFCCs), Mel spectrograms, and spectral variables like centroid, bandwidth, roll-off, and zero-crossing rate are used for feature extraction from the voice and lung dataset. These acoustic features train the transformer blocks and depth-adaptive parameters, enabling the model to capture complex patterns for effective signal classification. Along with this architecture, the model consists of frequency-focused attention mechanisms used to extract spectral characteristics that are most indicative of pathological conditions. Meanwhile, multiple pooling strategies are employed for the effective aggregation of temporal information. Due to this targeted design, the system serves as an effective clinical tool for classification, minimizing computational complexity and achieving an accuracy of about 83% in voice pathology classification and 99% in lung pathology classification.