2D Cardiac MRI Classification with 10-Fold Cross Validation on Deep Learning Model

Deep learning is still being explored at the moment, particularly in medicine. This tool has the ability to train on very large and complicated datasets. This capacity greatly aids researchers and medical professionals in their efforts to analyze cardiac Magnetic Resonance Imaging (MRI) more accurately and quickly. In the present study, we suggest a brand-new system for categorizing cardiac abnormalities. Sunnybrook Cardiac Data (SCD) training data was used to train and test the LeNet5 deep learning model. Hyperparameter tuning is applied before training. The model was tested using 10-folds cross validation, with performance results of 99 % accuracy. The novelty of the proposed method for classifying cardiac disease using cardiac MRI Images with the LeNet5 model.