2026-09-24T02:03:00Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/125648 2025-02-06 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms The student, Muhammad Huzaifa Suri, accepted the attached license on 2024-07-18 at 21:34. The student, Muhammad Huzaifa Suri, submitted this Thesis for approval on 2024-07-18 at 23:32. This Thesis was approved for publication on 2024-07-19 at 11:59. DSpace SAF Submission Ingestion Package generated from Vireo submission #21151 on 2025-02-04 at 21:05:27 AI-driven identification of melanoma risk factors using choroidal nevi retinal images Suri, Muhammad Huzaifa Khan 2024-07-19 Varatharajah, Yogatheesan Ai Machine Learning Ophthalmology Choroidal Nevus Choroidal Melanoma Fundus Images Risk Factors Interpretability eng This thesis explores the potential of using choroidal nevi retinal images to identify melanoma risk factors, employing advanced machine learning techniques. Utilizing a comprehensive dataset annotated by ocular oncology specialists, the study develops and validates models capable of distinguishing benign nevi from those at risk of transforming into melanoma. Our models achieve a peak Area Under the Curve (AUC) of 0.93 for identifying significant risk factors, outperforming baseline models such as ResNet-50. A significant focus of this research is on enhancing the interpretability of these AI models, ensuring that the diagnostic predictions are transparent and can be understood by clinicians. This approach not only improves trust in AI-driven diagnostics but also facilitates deeper insights into the decision-making process of the models. Moreover, the models demonstrate robust performance under various imaging conditions, including a maximum performance drop of only 5.28% at 40% zoom out, highlighting their utility in diverse clinical settings. The results demonstrate the efficacy of the models in identifying key risk factors and predicting nevi transformation, which could lead to earlier interventions and potentially improved patient outcomes in ophthalmology. This thesis sets the groundwork for future research aimed at integrating AI with traditional imaging techniques to create more robust, interpretable, and clinically applicable diagnostic tools. 2024-08 Thesis https://hdl.handle.net/2142/125648 Copyright 2024 Muhammad Huzaifa Suri Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign M.S. Thesis