EVALUATING THE EFFECTIVENESS OF PRE-TRAINED ENCODERS IN SEMANTIC SEGMENTATION FOR POLYP DETECTION

In this study, we explore the use of pre-trained encoders in semantic segmentation models for polyp detection within medical imaging, specifically focusing on endoscopic images.Polyp segmentation plays a crucial role in early diagnosis and treatment planning for colorectal cancer, making accurate segmentation models essential.Using U-Net as a base architecture, this study investigates the impact of several pre-trained encoders, such as on segmentation accuracy.Evaluation metrics, including Dice Coefficient, Intersection over Union (IoU), allow us to assess performance comprehensively.Results show that ResNet-50 achieves superior accuracy in capturing polyp boundaries, while lighter encoders like VGG-16 and VGG-19, excel in speed and are promising for realtime applications.