Aug 2026· International Journal of Engineering Research and Science & Technology· 0 citations
TL;DR
A deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture, which 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.
Abstract
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 is significant health burden and a major cause of death worldwide, hence the need to develop automated diagnostic systems with correctness and computational efficiency in order to rescue lives through early clinical intervention. This paper introduces a fine-grained deep transfer learning architecture to classify cardiac disease images into multiple classes using two large convolutional backbones ResNet50 and DenseNet121, which are trained systematically with AdamW, stochastic gradient descent (SGD), and Lion under a single experimental environment. The suggested pipeline combines standardized image preprocessing, stratified division of data, transfer learning hierarchical feature extraction based on the feature, hyper parameter convergence analysis, and 50-epoch supervised fine-tuning, and then thorough performance and efficiency assessment. The experimental findings indicate the stable optimization dynamic in all six backbone optimizer setups, with the most consistent validation convergence in ResNet50 with Lion optimizer. The most successful configuration obtained nearly 93.9% accuracy of validation, precision, recall, and F1-score and a robust ROC-AUC of 0.995, which verified a high inter-class separability and stable threshold-independent result. The loss and validation accuracy curves also show that the convergence is quick and the ability to generalize is high. In terms of deployment, DenseNet121 was found to have much lower architectural complexity (8.0M parameters) and reduced inference latency (8.4 ms/image) than ResNet50 (25.6M parameters, 12.8 ms/image), and yet have a competitive level of classification. The graph driven analysis also shows that optimizer selection mostly influences the smoothness of convergence, predictive calibration, but backbone architecture influences a tradeoff between representational richness and computational efficiency. On the whole, the suggested framework is a clinically applicable and deployment-focused solution to smart computer aided diagnosis of cardiac diseases.
Overall, AI-empowered echocardiography holds substantial promise for advancing precision diagnosis, risk stratification, and personalized management of HCM, facilitating a transition toward more intelligent and individualized cardiovascular care.
Miao Zhang, Shan-Shan Yuan, Hong-Yan Dai et al.· Frontiers in Cardiovascular...· 0 citations
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.
Chetana Shravage, Mukesh Dixit· International journal of com...· 0 citations
This study implemented strict subject-disjoint partitioning to eliminate data leakage, and simultaneously introduced cross-frame case aggregation to emulate the multi-frame visual synthesis process of expert echocardiographers, suggesting that the proposed workflow has the potential to serve as an adjunctive tool for septal defect screening.
Tao Zhang, Peipei Zhang, Qing-Yuan Zhang et al.· IEEE Access· 0 citations
Cardiomegaly, or enlargement of the heart, is a serious condition that often indicates underlying cardiovascular disease. Early detection is important because delayed diagnosis can lead to severe complications, including heart failure and increased mortality. Chest X-ray imaging is widely used as an initial screening tool; however, manual interpretation is time-consuming and depends heavily on clinical expertise. To address this challenge, this study presents an automated deep learning–based framework for detecting cardiomegaly from chest X-ray images. The dataset used in this research was collected from a publicly available Kaggle repository and includes both cardiomegaly and normal cases. All images were preprocessed using contrast enhancement and resizing to improve visual quality and model performance. The proposed model integrates convolutional neural networks with transformer and graph-based reasoning to capture both local anatomical features and global structural relationships of the cardiac region. Experimental results show that the proposed approach achieves superior performance compared with several baseline models, reaching an accuracy of 95.28% along with strong precision, recall, and AUC-ROC values. Additionally eXplainable AI analyses, including attention maps, Grad-CAM heatmaps, morphological assessment, and feature embedding, demonstrate that the model provides reliable and predictions.
A multimodal deep learning model integrating ECG and CXR significantly improved prediction of incident rVHD compared with ECG alone, with the greatest benefit observed for AR.
S. Li, B. Zhang, L. Pan et al.· medRxiv· 0 citations
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