Abnormality classification using convolutional neural network for echocardiographic images

Presently issues related to the heart are mostly considered as the deadliest diseases resulting in the increasing death rate across the globe irrespective of gender and age. Accurate and early prediction of these heart abnormalities require enormous experience along with advanced technology. Artificial Intelligence and Machine learning have contributed extensively as an emerging technology for the prediction of the diseases including heart abnormalities. However, still high accuracy of prediction and less computational complexities remains challenge for the researchers. Currently the prevailing scenario existing post pandemic and new variants coming in, it is very essential to come up with an amalgamation of technology and techniques as assessment for doctors in diagnosis. Proposed article makes use of Convolutional Neural networks (CNN) for detecting and classifying the heart abnormality into three labels as mild, moderate or severe which helps in appropriate treatment of the heart diseases. CNN model is built. The model is trained with datasets corresponding to mild, moderate and severe conditions of heart abnormality. The model is tested extensively for the test data and the performance indices are obtained are with reference to accuracy, precision, recall, specificity, F1-Score. Proposed work is implemented in the Keras libraries with Tensorflow 2.1.1 as backend. Furthermore, prediction performance and complexity overhead are correlated with the other existing cutting-edge algorithms in predicting the heart abnormalities. Results demonstrates that the proposed model proved to be efficient than the others with high prediction accuracy (99%) and less computational complexities.