Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Sayma Akther

Second Advisor

Saptarshi Sengupta

Third Advisor

William Andreopoulos

Keywords

Multi-Agent Reinforcement Learning, Deep learning, VGG16 Net- work, Deep Q-Network.

Abstract

Advances in medical diagnostics have increasingly harnessed the power of artificial

intelligence, offering substantial improvements in early and accurate disease identifi- cation. This paper elaborates on a novel integration of Multi-Agent Reinforcement

Learning (MARL) with Deep Learning for the early detection of skin cancer, one of the most prevalent and lethal forms of cancer when left unchecked. Our project capitalizes on the sophisticated VGG16 Network for the extraction of detailed features from the widely-utilized HAM10000 dermatoscopic dataset, enhancing these features with additional color, texture, and shape analysis. Utilizing a custom-designed MARL environment, we facilitate a collaboration among multiple intelligent agents, each harnessing Deep Q-Networks (DQN) for the optimization of decision-making processes. The results showcase our model’s proficiency, boasting an impressive accuracy rate of 94% in the classification of skin lesions. This indicates a significant leap towards a new era of computational assistance in dermatology, providing non-invasive, rapid, and reliable diagnostics for skin cancer.

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Available for download on Sunday, May 25, 2025

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