An Echocariogram based Heart Disease using Deep Learning Model

CDSS for Automated Cardiac MRI Diagnosis Using an Explainable Ensemble Deep Learning Model

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.

AUTOMATED HEART DISEASE DETECTION FROM ECHOCARDIOGRAPHIC IMAGE VIA DEEP NEURAL NETWORK

One of the primary causes of death worldwide is still heart disease. Although echocardiography is a commonly used method for identifying cardiovascular diseases, precise interpretation of echocardiogram pictures necessitates specialist medical knowledge. In order to overcome this difficulty, this paper presents a deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture. For medical picture analysis, EfficientNetB0 offers a lightweight yet effective solution thanks to its compound scaling technique, which balances network depth, width, and resolution. In order to lessen the need for human interpretation, the model is trained to automatically extract intricate and distinctive features from echocardiographic images. EfficientNetB0 is especially well-suited for real-time clinical use since it guarantees great accuracy at a cheap computing cost by utilizing its efficiency and good generalization potential. This strategy seeks to assist healthcare providers in enhancing diagnostic accessibility, consistency, and efficiency. The suggested approach has the potential to improve cardiovascular disease prognosis and early detection, thereby increasing the scalability of sophisticated diagnostic capabilities in a variety of healthcare settings.

Heart Disease Prediction Using EfficientNet-B0: A Deep Learning-Based ECG Image Analysis Approach

Autonomous Electrocardiogram (ECG) image classification contributes to the effective detection of cardiac abnormalities and decrease the reliance on manual interpretations. This research study integrates transfer learning and EfficientNet-B0 architecture for the classification of four-class ECG images. In the pre-trained model, the open-source ECG images are pre-processed via image size adjustment, tensor conversion, and channel wise normalization. EfficientNet-B0 architecture combined with the weights obtained via ImageNet training was fine-tuned by enabling a task specific classifier with four output classes and training with cross-entropy loss function and Adam optimizer. The model performance analysis is done in terms of accuracy, precision, recall, F1-score, and confusion matrix analysis. The resultant ECG image classification corresponds to the accuracy of 99.61%. The class-level performance demonstrates high model prediction results. Moreover, the proposed framework performs efficient classification and visualization of individual ECG images.

A novel approach for predicting heart failure survival using a rectangular coded network.

An Interpretable LES-Integrated Hybrid RNN-CNN Framework for Multimodal Cardiovascular Disease Prediction

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.

Detection of Cardiovascular Diseases Using Machine Learning and Deep Learning Techniques

CVDs are still a significant health problem worldwide and there is a need for an accurate and non-invasive diagnostic system for early clinical intervention. Automatic interpretation of electrocardiogram (ECG) images is difficult due to background interference (grid lines, waveform distortion and scanning artifacts). To overcome these issues, in this work, Present a Wavelet-CNN Hybrid (WCNN-H) framework for automatic multi-class classification of cardiovascular diseases from ECG images. The proposed method is a combination of two dimensional Discrete Wavelet Transform (DWT) with Daubechies (db2) mother wavelet for noise suppression and waveform enhancement and deep residual convolutional neural network for hierarchic feature extraction and classification. The framework was evaluated on the ECG Images Dataset of Cardiac Patients with 928 ECG image records in four diagnostic classes. The experiment achieved an overall classification accuracy of 98.10% using five-fold cross-validation, and precision, recall and F1-score for all classes were above 97%. The proposed framework has a high potential for intelligent computer aided cardiovascular screening applications.