Deep Learning Based Approach for Medical Image Segmentation: A Systematic Literature Review

In the field of medical imaging, deep learning has revolutionized image analysis by enabling advanced tasks such as anatomical structure segmentation, lesion detection, pathology classification, and clinical outcome prediction. This paper provides an exhaustive analysis of deep learning applications in medical image segmentation, reflecting on the methodical study selection of 149 articles guided by five classification criteria. It delves into the customization and adaptation of deep learning architectures across diverse medical imaging modalities, such as MRI and CT scans, underscoring their adaptability and wide-ranging applicability. Additionally, this study includes a meticulous assessment of research contributions from various geographical territories and organ-specific studies, illustrating the global and interdisciplinary impact of this technology. It offers a discourse on the principal applications of deep learning within the advanced healthcare sector, confronts the technical challenges and limitations inherent in medical image segmentation, and elucidates on the probable emerging trends and future research directions. The main focus of this systematic literature review is not only to present the current state-of-the-art in deep learning approaches in vogue for medical image segmentation but also to identify the most cutting-edge studies shaping the future of the field. Through this endeavor, the paper contributes valuable insights and provides a roadmap for future advancements in this crucial area of medical technology.