Light weight multi-branch network-based extraction and classification of myocardial infarction from 12 lead electrocardiogram images

Myocardial Infarction (MI) is a heart disease due to lack of blood and oxygen flow to some part of the heart muscle. MI is a leading cause of death globally. To decrease the likelihood of MI complications, it is essential to diagnose and treat MI promptly. A Light Weight Multi-branch Network (LW-MN) based feature extraction and classification of MI from Electrocardiogram image is proposed. Extracting the information from Electrocardiogram images without segmenting the leads separately may result in loss of information. Segmenting the 12 leads by detecting the text above each lead will extract the information accurately without any information loss. Light Weight model used to extract features and fusing all twelve leads features using the depth fusion method. The outcome from the fusion method is fed into DenseNet-161 classification algorithm. LW-MN model acquired a classification accuracy, specificity, sensitivity, and F1-score of 96.09%, 97.33%, 97.78%, and 95.18%.