Jul 2026· International Seminar on Intelligent Technology and Its Applications· pp. 844-849· 0 citations· 17 references
Abstract
Cardiovascular diseases remain a paramount global health crisis, necessitating early and precise diagnostic interventions. While medical imaging is the clinical standard, manual interpretation is highly susceptible to visual fatigue and inter-observer variability. This study proposes a novel, highly robust Computer-Aided Diagnosis (CAD) framework that overcomes the spatial and textural limitations of standalone Convolutional Neural Networks (CNN) in heart disease image classification. A Feature-Level Ensemble (Hybrid) architecture was created by putting together the deep semantic features of ResNet50V2, the spatial boundaries of VGG16, and the parameter efficiency of EfficientNetV2B3. To directly deal with the loss of features caused by anatomical background noise, a Convolutional Block Attention Module (CBAM) was added to the EfficientNet pathway. This gave the network two-dimensional (channel and spatial) visual attention. To guarantee a thorough and impartial assessment, a complete restructuring of a dataset comprising 5,977 images was undertaken using an 80:10:10 stratified split, thereby eliminating the accuracy paradox resulting from class imbalance. The proposed Hybrid CBAM model significantly outperforms standalone baselines, with a peak accuracy of 94.00%. For clinical use, it was very important that the attention-guided ensemble had a Recall (sensitivity) of 0.94 for finding pathological cases and a Negative Precision of 0.96. This study definitively demonstrates that the integration of multi-model feature extraction with focused visual attention mechanisms yields a highly sensitive, reliable, and non-invasive automated screening instrument for the early detection of cardiovascular disease.
FusionEye-Net combines strong internal performance with substantially improved cross-domain generalization through lightweight adaptation, underscoring the critical role of external validation and domain-aware fine-tuning in formulating trustworthy, adaptive AI-assisted decision-support tools that can safely augment automated ophthalmic screening workflows.
Ali M. Duhaim, A. M. Al-Bakry· Discover Artificial Intellig...· 0 citations
Coronary artery disease is one of the leading causes of death worldwide. There is a need for an efficient diagnostic system to detect CAD that can lead to effective clinical therapy. Existing deep learning models are mostly designed for either spatial imaging or temporal physiologic signal learning, but they have limited fusion ability and do not extract lesion-aware features. In response to these challenges, this study proposes an interpretable Local Energy Shape (LES)- integrated hybrid Recurrent Neural Network and Convolutional Neural Network (LES-RCNN) framework for cardiovascular disease prediction and classification using multimodal image and physiological datasets. The proposed framework implements LES-based feature extraction combined with recurrent learning and convolutional feature modeling to extract spatial, temporal, and lesion-based cardiovascular characteristics from CCTA, ECG image data, and structured CSV physiological records. Lesion localization is greatly enhanced by the structural representation provided by LES transformation. Also, the model's spatial and temporal feature learning is improved with a hybrid RNN-CNN architecture. Experimental assessment was carried out utilizing both CVD Atlas image datasets and UCI physiological datasets in binary and multi-label classification. The proposed 17-layer LES-RCNN architecture obtained 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-score. This was better than existing state-of-the-art cardiovascular disease prediction models. Through comparisons and convergence studies, it was confirmed that the learning was more stable, lost less, and had better generalization capacity. The results of the study reaffirm that the LES-based multimodal framework can provide an accurate, interpretable & computationally efficient diagnosis of early CVD & clinical decision support for clinical decision-making.
Indrapalli Swapna, Sasidhar Kothuru· International journal of com...· 0 citations
Medical imaging plays an essential role in the early detection and clinical assessment of multiple diseases; however, manual image interpretation is time-consuming and can be affected by inter-observer variability, particularly when subtle pathological patterns are present. Conventional convolutional neural networks (CNNs) provide strong local feature extraction but may inadequately capture long-range spatial dependencies, whereas Vision Transformer-based architectures effectively model global contextual relationships but can require substantial training data. To exploit their complementary capabilities, this study proposes a Hybrid CNN-Transformer Framework for Multi-Disease Detection from Medical Imaging Data. The proposed architecture employs a multi-scale CNN backbone to extract local texture, boundary, morphological, and lesion-level characteristics, followed by Transformer-based self-attention to capture long-range dependencies among spatial feature representations. An attention-guided feature-fusion module integrates local CNN features with global Transformer representations, and the resulting discriminative embedding is processed by a multi-class classification layer for disease prediction. Data augmentation, class-aware training, and regularization are incorporated to improve robustness under heterogeneous medical-image distributions. Under the proposed experimental configuration, the hybrid framework achieves an overall accuracy of 96.74%, sensitivity of 95.92%, specificity of 97.18%, precision of 96.31%, F1-score of 96.11%, and area under the receiver operating characteristic curve (AUC) of 0.986. Compared with the selected standalone CNN baseline, the proposed approach provides approximately 5.2% relative improvement in accuracy and 5.8% improvement in F1-score. The combined local-global representation also improves discrimination of visually similar disease categories compared with individual CNN and Transformer models. These findings demonstrate the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data. The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis.
M. Balakrishnan, K. Ananthi, S. R. et al.· International journal of com...· 0 citations
Automated classification of cardiac pathologies from cine-MRI remains a clinically significant challenge due to inter-patient morphological variability. This study presents a comparative evaluation of deep learning architectures and proposes an explainable ensemble framework for cardiac disease classification using the publicly available ACDC dataset. Seven pre-trained models were fine-tuned via transfer learning VGG16, MobileNet, EfficientNet, GoogLeNet, ResNet18, DenseNet, and Vision Transformer (ViT) on 1,468 cine-MRI images (80/20 train-test split). A soft voting ensemble combining the top-performing architectures was developed to improve generalization and diagnostic robustness. Among individual models, VGG16 achieved the strongest performance (accuracy: 97.28%, F1-score: 0.9643, precision: 0.9561, recall: 0.9726, specificity: 0.9726). The proposed ensemble model consistently outperformed all standalone architectures, yielding 98.23% accuracy, F1-score of 0.9762, precision of 0.9753, recall of 0.9771, and specificity of 0.9771, with a clinically relevant reduction in false negatives and an AUC of 0.9971. Explainability was ensured through complementary post-hoc analyses using SHAP, LIME, and Grad-CAM, collectively confirming anatomically coherent and clinically meaningful decision patterns. These results demonstrate that architectural diversity combined with probabilistic aggregation constitutes an effective and interpretable strategy for reliable cardiac MRI diagnosis in clinical decision support systems.
Soukaina Ait Ouaoures, Hayat Bihri, Salma Azzouzi et al.· EPJ Web of Conferences· 0 citations
With the rapid development of precision medicine and intelligent diagnostic technologies, automatic medical image classification has become an important tool for assisting clinical decision-making. However, substantial variations in lesion scale, complex long-range dependencies of tissue structures, and the need to capture subtle anatomical features present significant challenges to existing deep learning models. To address the limitations of the Swin Transformer in modeling multi-scale lesions and multi-level feature interactions, this study proposes a hierarchical complementary feature enhancement framework based on the Swin Transformer for medical image classification. The proposed architecture performs collaborative feature learning at three representation levels, including macro-scale lesion perception, global contextual interaction, and local detail refinement. Specifically, a Multi-scale Depthwise SE Block (MSD-SE Block) is introduced at the input of each stage of the Swin Transformer to enhance the model’s multi-scale feature representation capability. Subsequently, a Residual Convolutional Attention (RCA) module is integrated following the self-attention mechanism and the Multi-Layer Perceptron (MLP) to strengthen global contextual modeling, while a Local Detail Enhanced Residual Channel-Spatial Attention (LDERCSA) module is employed to refine subtle anatomical structures and discriminative local features. Through the coordinated interaction of these components, the proposed framework establishes a hierarchical feature enhancement mechanism that effectively improves medical image representation across multiple scales and feature levels. Comprehensive experiments were conducted on eight core subsets of MedMNIST v2, including BloodMNIST, BreastMNIST, DermaMNIST, OCTMNIST, OrganSMNIST, PathMNIST, PneumoniaMNIST, and RetinaMNIST, using an input resolution of 224
$$\times$$
224. Single-module comparison and ablation studies demonstrate that each proposed component contributes positively to the overall performance. Experimental results show that the proposed model achieves significant performance improvements on BreastMNIST, OCTMNIST, OrganSMNIST, and PneumoniaMNIST. Furthermore, comparative evaluations across all datasets confirm that the proposed framework exhibits strong robustness and generalization capability when processing multiple medical imaging modalities, including ultrasound, CT, X-ray, endoscopic, and microscopic images, thereby providing reliable technical support for computer-aided medical diagnosis systems.
Yachao Si, Yi Zhang, Ming-Zhan Zhao· Scientific Reports· 0 citations
Due to its late identification and challenging diagnosis, lung cancer continues to be one of the top causes of death for cancer patients globally, positioning it as one of the most critical concerns. Timely identification of cancerous nodules is essential for enhancing the patient’s survival likelihood CT image analysis by hand is not very productive and significantly relies on a specialist’s expertise. In this study, we offer an autonomous lung cancer classification method based on explainable deep learning. The popular DenseNet121 network serves as the foundation for our deep learning model, which is enhanced by the Convolutional Block Attention Module (CBAM). To improve feature extraction of significant spatial and channel properties of input data, attention techniques are added. Furthermore, our method is interpretable because the Grad-CAM technique makes it possible to explain the choices made by a machine learning system. A database of CT scans, comprising 4,598 pictures categorized by large cell carcinoma, adenocarcinoma, and healthy lungs, was utilized. Our evaluations show the model’s effectiveness with an accuracy rate of 94.6\%.
S. Jegadeesan, S. Matheswaran, R. Palanivelrajan· International Conference on...· 0 citations
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