Optimizing melanoma diagnosis: A hybrid deep learning and quantum computing approach for enhanced lesion classification

Abstract

Melanoma is one of the most aggressive forms of skin cancer, necessitating advanced diagnostic tools to improve early detection. This study presents a novel AI-driven approach that combines deep neural networks with quantum computing techniques for enhanced lesion classification. Specifically, we employ a U-Net model for segmentation and a hybrid Convolutional Neural Network - Quantum Neural Network (CNN-QNN) for classification. Our approach achieves a precision of 99.67 %, recall of 99.67 %, and an overall accuracy of 99.35 % on the HAM10000 dataset. Additionally, we report a sensitivity of 99.4 %, a specificity of 99.2 %, and a macro F1-score of 99.5 %, significantly surpassing traditional CNN-based classifiers. This hybrid model outperforms conventional deep learning approaches, demonstrating its potential for aiding dermatologists in clinical decision-making. A comparative analysis with state-of-the-art models further validates the effectiveness of our method.