Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation

Medical image segmentation is a vital task in medical imaging, aiming to extract meaningful and precise information from images. While traditional methods have been extensively used, they often suffer from drawbacks like poor accuracy and robustness. In contrast, deep learning methods, with their promising results in various applications, including medical image segmentation, are compared in this literature review. The review highlights the strengths and limitations of both approaches, providing insights into the current state-of-the- art techniques. Ultimately, it concludes that deep learning methods have demonstrated superior performance in medical image segmentation and are anticipated to make a significant impact in this field.