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.
Recommended Citation
Chercadu, Vikas, "Skin Cancer Detection using Reinforcement Learning" (2024). Master's Projects. 1403.
https://scholarworks.sjsu.edu/etd_projects/1403
