Automated Cardiovascular Disease Diagnosis from X-ray Images using Convolutional Neural Networks

Abstract

Problem

Cardiovascular Disease (CVD) is a leading cause of mortality worldwide, and its early and accurate diagnosis is crucial for effective treatment and patient care. Medical imaging, particularly X-ray imaging, plays a crucial role in the detection and assessment of cardiovascular abnormalities. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool in medical image analysis, demonstrating promising results in various diagnostic tasks. 

Proposed Approach

This research paper investigates the application of CNNs for the automated diagnosis of CVD from X-ray images. The CVD diagnosis framework proposed in this study consists of three key modules. The first module is an X-ray feature extractor built using a state-of-the-art CNN architecture. The second module is an age prediction component, which accurately estimates the age of the patients from the X-ray images. Finally, the third module is the CVD classifier, which categorizes the input X-ray images into four predefined severity categories of Cardiovascular Disease. 

Result

Through extensive experiments, the proposed method has demonstrated its capability to offer novel insights into the potential use of X-ray images for predicting systemic biomarkers in the diagnosis of CVD. I expect that the proposed CVD diagnosis method can provide a significant advancement in the field of cardiovascular healthcare by offering an accurate, efficient, and automated solution for early detection of CVD.