Quantum Machine Learning for Skin Cancer Classification

ABSTRACT

In todays world with the increasing rates of skin cancer, timely and accurate diagnosis is of utmost importance. It can greatly improve patient outcomes and lead to personalized treatment plans. Quantum computing is pushing the boundaries of technology and has the potential to solve medical problems more efficiently. Conventional machine learning techniques like deep neural networks are frequently employed for recognizing patterns in image data. However, quantum machine learning approaches are demonstrating significantly faster performance in the realm of medical image analysis. This project proposes a classification system based on Quantum Machine Learning that can classify skin lesion images into cancerous and non-cancerous classes. The publicly available Melanoma and Non-Melanoma datasets have been used to accomplish this task. This system could potentially help with early diagnosis of the disease and become a viable alternative until fully scalable quantum hardware becomes available. "In a world where skin cancer rates continue to rise, timely and accurate diagnosis is crucial for improved patient outcomes and personalized treatment plans. Quantum computing holds immense potential to address medical problems in a more efficient manner than existing classical machine learning methods. In particular, quantum machine learning methods have demonstrated faster processing speeds than classical techniques for medical image analysis. This project proposes a skin lesion classification system based on quantum machine learning, using publicly available melanoma and non-melanoma datasets. Such a system could aid in early diagnosis of the disease, and serve as a viable alternative until scalable quantum hardware becomes available."

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