Chatterjee, Priyadharshini, Siddiqui, Shadab, Abdul Kareem, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310 and Rao, Srikanth (2025) Attention-enhanced lightweight architecture with hybrid loss for colposcopic image segmentation. Cancers, 17 (5):781. ISSN 2072-6694 (Online) (doi:10.3390/cancers17050781)

Preview

PDF (Open Access Article) 49875 KAREEM_Attention-Enhanced_Lightweight_Architecture_With_Hybrid_Loss_(OA)_2025.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

Cervical cancer screening through computer-aided diagnosis often faces challenges like inaccurate segmentation and incomplete boundary detection in colposcopic images. This study proposes a lightweight segmentation model to improve accuracy and computational efficiency. The architecture integrates dual encoder backbones (ResNet50 and MobileNetV2) for high-level and efficient feature extraction. While a lightweight atrous spatial pyramid pooling (ASPP) module records multi-scale contextual information, a novel attention module improves feature details by concentrating on relevant locations. The decoder employs advanced upsampling and feature fusion for refined segmentation boundaries. The experimental results show exceptional performance: training accuracy of 97.56%, validation accuracy of 96.04%, 97.00% specificity, 96.78% sensitivity, 98.71% Dice coefficient, and 97.56% IoU, outperforming the existing methods. In collaboration with the MNJ Institute of Oncology Regional Center, Hyderabad, this work demonstrates potential for real-world clinical applications, delivering precise and reliable colposcopic image segmentation. This research advances efficient, accurate tools for cervical cancer diagnosis, improving diagnostic workflows and patient outcomes.

Item Type:	Article
Additional Information:	This article belongs to the Section Methods and Technologies Development.
Uncontrolled Keywords:	cervical cancer, image segmentation, contextual information, loss function, multi-scale feature extraction, refinement module
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) R Medicine > RG Gynecology and obstetrics
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	19 Nov 2025 15:08
URI:	https://gala.gre.ac.uk/id/eprint/49875

Actions (login required)

View Item

Downloads