Coronary Artery Disease Detection Using Deep Learning Algorithm

Coronary artery disease (CAD) is a chronic worldwide condition that necessitates precise and timely diagnosis to successfully treat it. Deep learning algorithms, specifically Deep Convolutional Generative Adversarial Networks (DCGANs), have shown great promise in medical images by producing synthetic images of quality. It examines the use of DCGANs in the detection of CAD by learning implicit patterns from images of coronary angiograms and the differentiation between normal and pathological cases. The model is trained with actual medical images and additionally enhanced with synthetic data produced by DCGANs for improved detection accuracy. Experimental results prove the effectiveness of the model in detecting CAD, depicting the role of generative models in computerized disease diagnosis. The method presents an alternative method of CAD detection with little reliance on large annotated data and increased diagnostic accuracy.