Comprehensive Review of Deep Learning Techniques in Medical Image Analysis

Deep learning has emerged as a transformative technology, rapidly becoming the preferred method for medical image analysis. Its capabilities in efficient and accurate object detection, segmentation, tracking, and classification of pathophysiological and anatomical structures provide critical support to clinicians in their routine workflows. By augmenting traditional diagnostic methods, deep learning applications enhance the accuracy, repeatability, and scalability of medical analyses, empowering physicians and facilitating rapid decision-making in clinical practice. While the potential of deep learning is amplified when trained on diverse and extensive datasets, the limited availability of sufficient medical images from healthcare institutions poses a significant challenge. This review explores solutions to address these limitations and highlights ongoing efforts to develop robust, deep learning-based computer-aided diagnostic tools. Applications in endoscopy, radiology, pathology, and dentistry are examined, emphasizing their potential to improve clinical workflows and ensure better patient outcomes.

کلیدواژه ها:

Deep Learning ، CNN ، Medical Image Analysis ، Image Segmentation ، GANS ، Artificial Intelligence