Deep Learning -Enhanced Image Segmentation for Medical Diagnostics

Picture segmentation is an essential instrument in medical diagnostics, as it enables the precise identification and examination of anatomical characteristics and pathological regions. Modern picture segmentation algorithms have been considerably improved in comparison to their predecessors as a result of recent developments in deep learning. This paper offers a succinct overview of the most recent developments in medical diagnostic image segmentation, which have been facilitated by the application of deep learning algorithms. U-Nets, CNNs, and FCNs are deep learning architectures that are frequently implemented in medical imaging. The precision and efficacy of automated image analysis have been improved by integrating these models into medical diagnostics. Furthermore, we investigate the challenges that arise when applying deep learning models to healthcare, including the necessity for a more comprehensive comprehension of the models and the restricted availability of data.