AUTOMATIC CARDIAC MRI CLASSIFICATION USING DEEP RESIDUAL NETWORKS

Due to the excellent spatial resolution that cardiac magnetic resonance imaging (CMR) offers, it is possible to better extract crucial functional and morphological aspects for the staging of cardiovascular illness. CMR, with its great spatial resolution, allows for the improved extraction of crucial functional and morphological elements for the staging of cardiovascular disease. Cardiologists employ CMR to assess temporal geometric changes and manually estimate heart function by outlining forms. Yet, this work demands a great deal of accuracy and takes a long time. For cardiac analysis, deep learning techniques have been widely used. The stages proposed in this study include (i) converting 3-dimensional images into 2-dimensional ones, (ii) obtaining contour values from ground truth images, (iii) cropping the image localization area to obtain the region of interest (RoI), (iv) dataset separation to train, validate, and test data (70, 20, 10), and (v) classify using the ResNet50 V2 model. The ACDC MICCAI 2017 dataset is used in this study to classify the cardiac into five main categories abnormal and one healthy the ResNet50 V2 architecture. By having a measurement accuracy of 0.98, performance outcomes are indicated. Experimental results show the robustness of the proposed architecture. ICIC International © 2024.