Abbas Fadhil Salih
High Institute of infertility Diagnosis and ART · Al-Nahrain University
Biography
Abbas Fadhil Salih is a PhD researcher specializing in computer vision, digital medicine, and biomedical image analysis. His work focuses on the development of hybrid deep learning frameworks for the segmentation of linear anatomical structures, particularly retinal vessels. By integrating U-Net architectures with Extreme Learning Machines, self-supervised learning, and transformer-based mechanisms, he has proposed innovative solutions that achieve high accuracy with limited data, while ensuring computational efficiency and deployability in real-world healthcare environments. His research bridges algorithmic design with practical software solutions, contributing to both theoretical advancement and clinical impact in digital ophthalmology.
Education
PhD (Ongoing): Computer Science (Biomedical Image Analysis & AI in Digital Medicine). Dissertation focus: Hybrid deep learning and self-supervised frameworks for segmentation of retinal vascular structures.Ural Federal University, Yekaterinburg, Russia (2021-2025) M.Sc. Computer Science / Software and Information Systems Ural Federal University, Yekaterinburg, Russia (2016–2017) Thesis: Distributed Computing for Big Data B.Sc. Computer Science Baghdad College for Economic Sciences, Baghdad, Iraq (2002–2006) Professional Training Courses: Photoshop (Al Nahrain University, College of Enginee...
Recent Google Scholar Publications
- Multi-Criteria Decision Making For Environmental Monitoring Applications Using Fuzzy Optimization Algorithm
- Analysis and evaluation of symmetric key ciphers for internet of things smart home
- Statistical Analysis of Organizational Performance and Its Effects on the Income Levels: An Exploratory Study of the Higher Institute for the Diagnosis of Infertility and …
Links
- ORCID https://orcid.org/0000-0001-5292-8466
- Google Scholar https://scholar.google.com/citations?user=yL00cJIAAAAJ
- Scopus https://www.scopus.com/authid/detail.uri?authorId=No