A Comprehensive Review of Deep Learning and Machine Learning Techniques in Medical Image Analysis

This comprehensive review paper examines deep learning and machine learning techniques applied to medical image analysis. The study systematically analyzes 26 major research papers from peer-reviewed venues, cataloging methodologies including convolutional neural networks (CNNs), generative adversarial networks (GANs), vision transformers, and other advanced architectures. These techniques are evaluated for disease detection, image segmentation, and classification across diverse imaging modalities such as CT, MRI, X-rays, and mammography. Through systematic analysis, the paper identifies key techniques, performance characteristics, challenges, and future directions in medical imaging. Findings demonstrate that deep learning models significantly enhance diagnostic accuracy and clinical decision support; however, critical challenges remain related to dataset quality, model interpretability, and regulatory compliance. The study concludes that integrating advanced machine learning techniques with medical expertise can substantially improve healthcare outcomes, while highlighting the necessity for continued research in explainable AI, federated learning, and privacy-preserving approaches for practical clinical deployment.