ICCK

Omneya Attallah

Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt

Section 01

Academic Profile

Omneya Attallah is a Professor with the Department of Electronics and Communication Engineering at the Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt. She received her Ph.D. in Electrical and Electronics Engineering from Aston University, UK, in 2016. She is the founding member of the WeBios Lab (Wearables, Biosensing, and Biosignal Processing) at AASTMT. Prof. Omneya has been recognized as one of the top 2% of World Scientists based on the prestigious Stanford/Elsevier database (2022–2024) and ranked among the top 0.05% of lifetime scholars in Deep Learning by ScholarGPS. Her research focuses on AI-driven technologies in medical diagnosis, healthcare, and biosignal analysis. She has led and participated in several funded research projects. She is an IEEE Senior Member, a professional ACM Member, and a full member of Sigma Xi and OWSD. She has received numerous awards, including the Falling Walls Women's Impact Award 2025 and the Bioinformatics Sciences Award 2023. She also received several travel grants from IEEE and ACM. Prof. Omneya serves as Editor-in-Chief of the Journal of Computational Intelligence in Biomedicine and as Associate Editor for multiple journals, including Scientific Reports, International Journal of Imaging Systems and Technology, and Journal of Computational Intelligence Systems. She is also a guest editor and reviewer for leading journals published by IEEE, Springer, Elsevier, MDPI, and Wiley, and actively contributes to international conferences as a technical committee member.

Section 02

Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

Section 03

ICCK Publications

Open Access | Editorial | 16 April 2026
Artificial Intelligence in Breast Cancer Diagnosis: Current Trends, Limitations, and Future Prospects
Journal of Computational Intelligence in Biomedicine | Volume 1, Issue 1: 10-23, 2026 | DOI: 10.62762/JCIB.2025.683401
Abstract
Breast cancer continues to be a predominant cause of cancer-related fatalities among women worldwide. Timely and precise diagnosis is essential for successful intervention and enhanced patient outcomes. Artificial intelligence (AI), especially deep learning (DL) methodologies, is swiftly revolutionizing breast cancer diagnostics, providing unparalleled prospects to improve the accuracy and efficacy of detection and characterization. This editorial paper explores the crucial role of AI in breast cancer imaging, analyzing its utilization in computer-aided diagnosis (CAD) and its capacity to address the intrinsic limits of manual assessment. The article will examine several DL approaches uti... More >