Abstract This literature review examines the advancements in automated semantic segmentation in X-ray medical imaging through the lens of deep learning. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach, the review demonstrates the transformative effect of deep learning technologies in enhancing diagnostic accuracy and efficiency in medical imaging, significantly influencing healthcare outcomes. X-ray imaging is essential in medical diagnostics because it is a non-invasive and highly informative diagnostic tool. This review explores the application of deep learning to automate the examination of small, targeted segments of the torrent of information produced by X-ray imaging. The review delves into the specific role of deep learning algorithms in identifying and labeling anatomical structures and pathologies in X-ray images, underscoring the precision and sophistication these models bring to medical imaging. The discussion on automatic segmentation centers around how deep learning algorithms effectively delineate specific regions of interest, thereby facilitating more advanced analysis and interpretation. The review concludes with directions for future research in this field, emphasizing the growing importance and potential of deep learning in X-ray image processing. This comprehensive synthesis provides researchers and practitioners with a clear understanding of the current state and prospects of deep learning in automated X-ray image processing.
Abueed et al. (Sun,) studied this question.