Evaluating the Effectiveness of Pre-trained Encoders in Semantic Segmentation for Polyp Detection
Year: 2024
Type: article
Abstract: 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 VGG-16, ResNet-50, and VGG-19, 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.
Authors: Rohit Srivastava, Manish Madhava Tripathi
Institution: Integral University
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4405865138