Modification of U-Net with Pre-Trained ResNet-50 and Atrous Block for Polyp Segmentation: Model TASPP-UNet

Abstract

Colorectal cancer is the third most prevalent type of cancer globally, and it typically progresses unnoticed, making early detection via effective screening methods crucial. This study presents the TASPP-UNet, an advanced deep learning model that integrates Atrous Spatial Pyramid Pooling (ASPP) blocks and a ResNet-50 encoder to enhance polyp boundary delineation accuracy in colonoscopy images. We utilized augmented datasets from Kvasir-SEG and CVC Clinic-DB, which included up to 2000 images, to enrich the training examples’ variability. The TASPP-UNet achieved a superior IOU of 0.9276, compared to 0.9128 by the ResNet50-UNet and 0.8607 by the standard U-Net, demonstrating its efficacy in precise segmentation tasks. Notably, this model exhibited impressive computational efficiency with a processing speed of 151.1 frames per second (FPS), underscoring its potential for real-time clinical applications aimed at early and accurate colorectal cancer detection. This performance highlights the model’s capability not only to improve diagnostic accuracy but also to enhance clinical workflows, potentially leading to better patient outcomes.

Keywords

• deep learning
• polyp segmentation
• U-Net
• atrous block