AI-driven identification of melanoma risk factors using choroidal nevi retinal images
Suri, Muhammad Huzaifa Khan
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https://hdl.handle.net/2142/125648
Description
Title
AI-driven identification of melanoma risk factors using choroidal nevi retinal images
Author(s)
Suri, Muhammad Huzaifa Khan
Issue Date
2024-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Varatharajah, Yogatheesan
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Ai
Machine Learning
Ophthalmology
Choroidal Nevus
Choroidal Melanoma
Fundus Images
Risk Factors
Interpretability
Language
eng
Abstract
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.
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