Advanced cardiovascular disease classification using multi-modal imaging and deep learning

Abstract

Cardiovascular disease (CVD) is a disorder of the heart and blood vessels that causes significant morbidity and mortality. They also represent a global public health burden and the primary cause of death worldwide. In this research, a novel deep learning-based multi-model image (DL-MMI) has been proposed for detecting CVD. Initially, the input Kaggle datasets images like magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and chest X-ray are fed into wavelet transform-based Multiscale Retinex in the pre-processing phase to enhance the quality of the images. Then the enhanced images are given to GLCM for extracting features in the images. Finally, the dilated convolutional neural network (D-CNN) is used to classify healthy and CVD images. The experimental findings use the specific measures of accuracy, recall, precision, specificity, and F1-score to demonstrate the durability of the DL-MMI approach. Using the Kaggle dataset the proposed DL-MMI method achieves an accuracy rate of 98.89%. The proposed DL-MMI model increases the overall accuracy by 28.62%, 7.51%, and 17.57% than the existing methods such as convolutional auto encoder, CNN, and deep learning, respectively.