Quantum computing meets skin cancer diagnosis

Daniel Alejandro Lopez; Oscar Montiel; Miguel Lopez-Montiel; Oscar Castillo

doi:10.1117/12.2675446

4 October 2023 Quantum computing meets skin cancer diagnosis

Daniel Alejandro Lopez, Oscar Montiel, Miguel Lopez-Montiel, Oscar Castillo

Proceedings Volume 12673, Optics and Photonics for Information Processing XVII; 126730J (2023) https://doi.org/10.1117/12.2675446
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Skin cancer is one of the most common and lethal diseases in America, and its early detection and treatment is the best approach to protect the lives of those afflicted. Computer-Aided Diagnosis systems have been implemented for decades as evidence of intelligent methods applied in the medical field. In recent years, machine and deep learning fields have shown great potential in medical diagnosis, particularly in skin cancer classification. These methods enable the automated extraction of complex input features, such as those in medical imagery. Therefore, with the advent of quantum computing, it is now possible to perform complex computations with increased speed and efficiency. This study explores the application of quantum machine learning in the classification of skin cancer using the ResNet50 model, a deep convolutional neural network for RGB images. The research employs a quantum-enhanced version of ResNet50 using a quanvolutional layer that the input skin lesion images go through and compare its performance with the classical ResNet50 version. We show comparative experiments, and the results indicate that further experiments are needed using more extensice datasets and different quantum deep learning architectures.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Daniel Alejandro Lopez, Oscar Montiel, Miguel Lopez-Montiel, and Oscar Castillo "Quantum computing meets skin cancer diagnosis", Proc. SPIE 12673, Optics and Photonics for Information Processing XVII, 126730J (4 October 2023); https://doi.org/10.1117/12.2675446

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