Enhancing Skin Lesion Classification Performance via Hybrid Quantum-Classical Learning

Abstract (excerpt). Accurate skin disease classification from medical images is challenging due to data complexity. This paper proposes a novel hybrid classical-quantum framework to improve the performance of classical deep learning approaches on the skin lesion classification problem. This research conducts experiments on the HAM10000 dataset, a dataset that comprises skin lesion images for the classification task. To leverage the quantum advancements but still have limitations of the noisy intermediate-scale quantum era, we design a variational quantum circuit (VQC) architecture for enhancing the performance of classical features on HAM10000 dataset. We also compared the performance of some well-known pretrained deep learning architectures to demonstrate the advancement of our proposed hybrid classical-quantum approach. By using this approach, we enhanced the accuracy of a classical deep learning model from …

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Recommended citation: Trung Thanh Pham, Phuong-Nam Tran, Choong Seon Hong. (2025). "Enhancing Skin Lesion Classification Performance via Hybrid Quantum-Classical Learning." Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 1611-1613.
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