Better Network Optimization Through Batch Normalization in Left Ventricle Chamber Classification

Convolutional neural networks (CNNs) have emerged as a prominent deep learning technique for medical image classification. This study investigated the impact of batch normalization layer placement on the performance of the CNNs model in classifying the left ventricle segment in Delayed-enhancement cardiac magnetic resonance (De-CMR) image slices. Three batch normalization arrangements, including one without a batch normalization layer, were examined to assess their impact. Additionally, the influence of three learning rates (0.0001, 0.001, 0.01) from two different types of optimizers, namely Adam and Sgdm, was explored to identify the optimal configuration for our proposed CNN model. A model without batch normalization was used as a baseline for comparison. The results show that placing batch normalization after the convolutional layers, combined with the Adam optimizer and a learning rate of 0.0001, yielded the best performance, improving classification accuracy from 83.1% to 88.4%. These results highlight the significance of batch normalization layers with optimal configuration in enhancing the performance in the classification of the left ventricle and non-LV chambers in De-CMR images, thereby facilitating improvements in the streamlined workflow for automated myocardial infarction diagnosis.