Quantum classical convolutional neural network using ResNet-18 and feature optimization for multiclass skin lesion classification

Skin cancer is among the most widespread and life-threatening conditions in the world with the need to detect lesions promptly and consistently in order to treat the disease in a timely manner. Nonetheless, automated lesion classification of the skin is a complex challenge due to inter-class similarity, which is high, illumination variation, and imbalance in the data in various classes of lesions. To address these obstacles, this paper presents a lightweight but powerful 6-qubit Hybrid Quantum-Classical Convolutional Neural Network (HQNN) that is optimized to 6-bit (low-precision) medical imaging applications. The proposed framework will use a ResNet-18 backbone to learn deep dermatological features based on dermoscopic images and a Particle Swarm Optimization (PSO) algorithm with a Random Forest (RF) ranking of features will reduce the 512-dimensional embedding space into six highly discriminative descriptors, which can be encoded in quantum. These are then converted into quantum states with ZZFeatureMap and RealAmplitudes ansatz which comprise a 6-qubit variational circuit the output of which is fused with a classical CNN classifier to achieve final lesion prediction. This strategy of optimization of quantum-aware features is a balance between the representation of high dimensions and limitations of qubits, which ensures a computational tractability on near-term noisy intermediate-scale quantum (NISQ) devices. Existing experimental results on the ISIC 2019 skin-lesion dataset show state-of-the-art performance achieving 98.2% accuracy, 98.5% precision, 97.7% recall, 98.0% F1-score, and a macro ROC-AUC of 0.985. The findings affirm that quantized deep learning with low-qubit quantum encoding has better diagnostic performance, noise resistance, and scalability with minimal hardware costs, making Q-SkinNet a realistic advancement towards experimental quantum-assisted dermatological diagnostics.