Early Stage Detection of Colorectal Cancer using Segmentation of Polyps

Document Type

Conference Proceeding

Source of Publication

2nd International Conference on IT Innovations and Knowledge Discovery Itikd 2024

Publication Date

5-21-2025

Abstract

Colorectal Cancer(CRC) is a major health problem around the world, accounting for a high number of cancer related deaths. It is important to detect the polyp at the initial stage and remove it to prevent them from becoming cancerous. Colonoscopy is the standard examination for CRC, but pathologists face difficulties in detecting the polyps in the colonoscopy image due to the small size and the color contrast between the polyp and its background. Expert pathologists are very less in numbers compared with the cancer patients.therefore, an automated system is required to assist pathologists in the detection of polyps in early stage. Deep learning models help the pathologists to detect polyps at an early stage automatically that may be missed otherwise due to their small size, low contrast, and presence of extremely small polyps in a single image. We propose light weight model Transformer ResU- Net3plus (TransResU-Net3+) for automated segmentation of polyps in early stage of cancer. Proposed method consists of residual blocks using ResNet-50 as the backbone and also uses transformer self-attention and dilated convolutions. We have applied the proposed method on a publicly available dataset kvasir-seg and achieved an Intersection Over Union (IOU) of 0.892 and outperforms over existing state of the art methods on kvasir-seg dataset.

ISBN

[9798350355468]

Publisher

IEEE

Disciplines

Medicine and Health Sciences

Keywords

CNN, Colorectal Cancer, Deep learning, Polyp, Segmentation, Transformer Residual U-Net, U-Net 3 Plus

Scopus ID

105007517553

Indexed in Scopus

yes

Open Access

no

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