An Echocariogram based Heart Disease using Deep Learning Model

Abstract

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.