A master’s thesis was discussed at the University of Ninevah / College of Electronics Engineering / Department of Computer and Information Engineering by the student Abdulrahman Dhanoon Abdulrahman on Thursday, 05/02/2025, entitled:
Machine Learning Approaches for Keratoconus Detection
The thesis investigated the effectiveness of lightweight convolutional neural network architectures in the automated detection of keratoconus using color-coded corneal tomography maps.
The study aimed to propose machine learning and deep learning models characterized by low complexity and small size while maintaining high classification accuracy for keratoconus, with the goal of enabling early disease detection.
The thesis concluded that the proposed frameworks provide a foundation for effective and accessible diagnostic tools, facilitating early detection and supporting timely clinical intervention.
The examination committee consisted of:
1. Prof. Dr. Mohammed Sabah Jirjis – Northern Technical University / Technical Engineering College (Chair).
2. Asst. Prof. Dr. Sidqi Bakr Dhanoon – University of Ninevah / College of Electronics Engineering (Member).
3. Lect. Dr. Fares Saleh Fathi – University of Ninevah / College of Electronics Engineering (Member).
4. Asst. Prof. Dr. Mohammed Abdulmutalib Mohammed – University of Ninevah / College of Electronics Engineering (Member and Supervisor).
5. Lect. Dr. Bashar Iyad Al-Hayali – University of Ninevah / College of Medicine (Member and Supervisor).
The discussion was attended by the President of the University of Ninevah, Prof. Dr. Osama Al-Mashhadani, and the Dean of the College of Electronics Engineering, Prof. Dr. Khalid Khalil Mohammed.
