A comprehensive review on applications of computer vision in medical imaging

Medical imaging has become an essential tool in healthcare, offering non-invasive methods for diagnosing and monitoring diseases. Recent advancements in computer vision have enabled significant improvements in the analysis and interpretation of medical images. This review explores the applications of computer vision in medical imaging, focusing on tasks such as image segmentation, disease diagnosis, and 3D reconstruction. Key deep learning methods, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), are discussed in terms of their effectiveness in enhancing diagnostic accuracy and automating analysis. Additionally, challenges such as data scarcity, privacy concerns, and the need for explainable AI models are addressed. The review concludes with a discussion on future directions in medical image analysis, including the integration of multimodal learning and improvements in model interpretability.