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 Copy

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

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.

Graduation Semester

2024-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/125648

Copyright and License Information

Copyright 2024 Muhammad Huzaifa Suri

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