Work

Polyp Image Segmentation Using Deep Learning Techniques: Resunet++ Architecture

Year: 2025

Type: article

Abstract: This study presents a novel polyp segmentation approach using ResUnet++. Trained on Kvasir-SEG and CVC-ClinicDB, ResUnet++ significantly outperforms traditional UNet and ResUnet. Its residual blocks and attention mechanisms enhance feature extraction, leading to improved segmentation in challenging cases. This highlights the potential of deep learning for advancing polyp segmentation and improving early colorectal cancer detection. Future research could explore further modifications or alternative architectures.

Source: Tạp chí Khoa học HUFLIT

DOI: 10.71091/2354-113x/225

Author: Tram Tran Nguyen Quynh

Institution: Ho Chi Minh City University of Foreign Languages - Information Technology

Language: English

Topic: Colorectal Cancer Screening and Detection

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W4409443252

FWCI:

Cites: 15

Cited by:

Related to: 10