Medical image segmentation

Abstract

Medical image segmentation is a pivotal technique in modern medical imaging, essential for enhancing diagnosis accuracy and patient care. This study explores the application of four advanced deep learning models - DermoSegDiff, DCSAU, Unimatch, and Unet++ - for semantic segmentation in medical imaging. Semantic segmentation involves labeling each pixel in an image, a crucial step in identifying regions of interest such as tumors and organs. Our research implements these models on four distinct datasets: ISIC-2018, PH2, Data Science Bowl-2018, and SegPc-21. The findings demonstrate that these models achieve high accuracy in segmentation tasks, underlining their potential to revolutionize medical imaging analysis and diagnosis. This report not only sheds light on the capabilities of automated image segmentation but also emphasizes its significance in improving patient treatment and monitoring. The study contributes to the ongoing advancement of automated medical image analysis, highlighting the promise of deep learning in enhancing patient care and diagnostic precision.