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Abstract

Automated analysis of dermoscopic images can support benign-versus-malignant skin lesion classification, but the practical usefulness of hybrid quantum-classical models remains unclear under current hardware constraints. This paper presents an experimental comparison between a classical MobileNetV2-based baseline and a hybrid pipeline that combines convolutional feature extraction with a variational quantum circuit for binary classification on the publicly available HAM10000 dataset. In the hybrid approach, image features extracted by the classical backbone are projected to a low-dimensional latent space and encoded into a trainable quantum circuit. Both models are evaluated on disjoint train, validation, and test splits, with model selection for the quantum pipeline performed on the validation set. The classical baseline clearly outperformed the hybrid model. The selected hybrid configuration reached a balanced accuracy of 0.582 and an area under the receiver operating characteristic curve (AUC) of 0.610 on the held-out test set. We also validated inference on IBM Quantum backends, confirming compatibility with execution on real quantum hardware. Taken together, these results suggest that the hybrid pipeline is better viewed as a feasibility study under current noisy intermediate-scale quantum (NISQ) limitations rather than as a replacement for strong classical image classifiers.

Citation

Panca, A.-G., Ciorătanu, M., Balauca, S., & Iftene, A. (2026). Experimental Comparison of a Classical CNN and a Hybrid Quantum-Classical Model for Dermoscopic Skin Lesion Classification. 30th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems.

@inproceedings{panca2026skin,
  title = {Experimental Comparison of a Classical CNN and a Hybrid Quantum-Classical Model for Dermoscopic Skin Lesion Classification},
  author = {Panca, A.-G. and Ciorătanu, M. and Balauca, Stefan and Iftene, Adrian},
  year = {2026},
  booktitle = {30th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems},
}