Alaa Youssef
Research Subjects
Alaa Youssef has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Aliaa A. A. Youssif
Arab Academy for Science, Technology, and Maritime Transport, Smart Village Campus
1 shared publication - Wessam Hassan El Behaidy
Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Cairo 11795, Egypt
1 shared publication
Latest Publications
- 3 December 2025
- Published by International Academic Press in Statistics, Optimization & Information Computing
- Vol. 15 (3) , 2026-2046
CancerSeg-XA: Medical Histopathology Segmentation System Based on Xception Backbone and Attention Mechanisms
Accurate segmentation of histopathological images is essential to support early diagnosis and effective treatment planning in cancer care. This study presents CancerSeg-XA, a deep learning-based histopathology segmentation system designed to deliver robust performance across diverse tissue types and imaging sources. Built upon the DeepLabV3+ framework, CancerSeg-XA incorporates architectural enhancements to strengthen feature representation and improve model stability. The system was evaluated on three widely recognized datasets—BCSS, PanNuke, and PUMA—each presenting distinct structural and clinical challenges. Across all datasets, CancerSeg-XA consistently outperformed the baseline DeepLabV3+ in terms of segmentation accuracy, recall, and F1-score. Specifically, it achieved accuracy improvements of 4.78%, 4.31%, and 3.22% on BCSS, PanNuke, and PUMA, respectively, along with substantial gains in FwIoU. These results highlight the model’s ability to generalize effectively across varied histopathological contexts, positioning CancerSeg-XA as a promising solution for clinical integration and future research in digital pathology.
- 3 December 2025
- Published by International Academic Press in Statistics, Optimization & Information Computing
- Vol. 15 (3) , 2026-2046
CancerSeg-XA: Medical Histopathology Segmentation System Based on Xception Backbone and Attention Mechanisms
Accurate segmentation of histopathological images is essential to support early diagnosis and effective treatment planning in cancer care. This study presents CancerSeg-XA, a deep learning-based histopathology segmentation system designed to deliver robust performance across diverse tissue types and imaging sources. Built upon the DeepLabV3+ framework, CancerSeg-XA incorporates architectural enhancements to strengthen feature representation and improve model stability. The system was evaluated on three widely recognized datasets—BCSS, PanNuke, and PUMA—each presenting distinct structural and clinical challenges. Across all datasets, CancerSeg-XA consistently outperformed the baseline DeepLabV3+ in terms of segmentation accuracy, recall, and F1-score. Specifically, it achieved accuracy improvements of 4.78%, 4.31%, and 3.22% on BCSS, PanNuke, and PUMA, respectively, along with substantial gains in FwIoU. These results highlight the model’s ability to generalize effectively across varied histopathological contexts, positioning CancerSeg-XA as a promising solution for clinical integration and future research in digital pathology.