AI-Driven Cardiomegaly Detection Using Cutting-Edge Chest X–ray imagery

Abstract

This project aims to develop an innovative approach for detecting cardiomegaly, a condition characterized by an enlarged heart, using machine learning techniques, specifically Support Vector Machines (SVM), and state-of-the-art chest X-ray imagery. Cardiomegaly serves as a significant indicator of various cardiac diseases, and early detection through accurate imaging analysis can lead to timely intervention and improved patient outcomes. The proposed machine learning solution utilizes SVM algorithms trained on a diverse dataset of chest X-ray images to automatically identify signs of cardiomegaly with high precision and reliability. By harnessing the power of SVM, this system offers a non-invasive and cost-effective method for screening large populations for cardiac abnormalities, facilitating early diagnosis and appropriate medical management. Key features of the developed solution include robust feature extraction techniques, real-time analysis, and integration with existing healthcare infrastructure for seamless implementation in clinical settings. Moreover, the system prioritizes patient privacy and data security by adhering to relevant regulations and standards governing medical imaging and information management. Overall, this project represents a significant advancement in the field of cardiac health monitoring, providing healthcare professionals with a valuable tool for early detection and management of cardiomegaly, ultimately improving patient care and reducing the burden of cardio vascular disease