DEEP LEARNING IN MEDICAL IMAGING: A COMPREHENSIVE REVIEW OF DIAGNOSTIC SYSTEMS

Abstract

Deep learning has emerged as a transformative approach in medical imaging, offering unprecedented accuracy and efficiency in diagnostic applications. This comprehensive review examines the integration of deep learning techniques—particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs)—into various medical imaging modalities such as MRI, CT, X-rays, ultrasound, and PET scans. The study highlights key advancements in image classification, segmentation, detection, and synthesis, emphasizing how deep learning models enhance early diagnosis, reduce human error, and support clinical decision-making. It also explores challenges such as data scarcity, model interpretability, and ethical considerations, while suggesting future directions for integrating deep learning with other technologies like federated learning and explainable AI. This review aims to guide researchers, developers, and healthcare practitioners toward more reliable and scalable diagnostic solutions using deep learning in medical imaging.