REAL-TIME HEART DISEASE DETECTION USING YOLO AND DEEP LEARNINGBASED FEATURE EXTRACTION

This study presents a innovative approach for real-time heart disease detection using YOLOfor object detection and deep learning-based feature extraction. The proposed system aims to enhance the accuracy and efficiency of heart disease diagnosis by leveraging advanced computer vision techniques. The methodology involves preprocessing medical images, such as echocardiograms and chest X-rays, followed by the application of the YOLO algorithm to detect regions of interest (ROIs). A deep learning model, specifically a CNN, is then used to extract features from these ROIs. The extracted features are subsequently used to classify the presence and type of heart disease using a ML classifier. Dataset employed for both training and evaluationincludes a diverse set of medical images labeled with various heart disease conditions. The performance of the system is evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC, demonstrating its potential for real-time clinical applications. This research contributes to the field of medical imaging and diagnosis by providing a robust and efficient tool for heart disease detection. Keywords: Real-Time Heart Disease Detection, YOLO (You Only Look Once), Deep Learning, Feature Extraction, Medical Imaging