Lesion-Based Detection of Cardiovascular Diseases Using Deep Learning and Red Deer Optimization

Abstract

Nowadays, cardiovascular disease is a very concerning health issue in human life. Medical imaging through MRI plays an important role in the detection of many diseases. Magnetic resonance imaging (MRI) is a non-invasive and sophisticated diagnostic tool for cardiovascular disease (CVD) that allows for full visualization of the heart and blood vessels. Through Magnetic resonance imaging, we get high-quality images of blood vessels, which helps in detecting various types of heart-related diseases. With the help of MRI, we can detect various types of heart-related diseases. It also gives us information about their early diagnosis and their preventive measures. Deep learning and its advanced features are proving to be very helpful in this work. Deep learning has brought many new changes in this field. The article presents the Red Deer Optimizer with Deep Learning (ACVD-RDODL) algorithm for automated cardiovascular disease identification using magnetic resonance imaging (MRI). The primary goal of the proposed approach is to use Deep Learning models on cardiac MRI to detect Cardiovascular issues. The dynamic histogram equalisation (DHE) based noise removal model is used in the given approach to pre-process the images. Additionally, the Attention Based Convolutional Gated Recurrent Unit Network (ACGRU) model is used in this approach to classify Cardiovascular disease