ArticleScientific reports2025
A hybrid quantum-classical convolutional neural network with a quantum attention mechanism for skin cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Decrypting chaotic visual ciphers via quasi quantum neural networks (Q²NNs).Scientific reports · 2026Article
- VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology using X-ray angiography.Frontiers in artificial intelligence · 2026Article
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Abstract
Skin cancer is the most common and fatal illness globally, and therefore, proper early detection is essential for successful treatment and enhanced patient outcomes. Classic deep learning models, especially Convolutional Neural Networks (CNNs), have greatly succeeded in medical image classification. However, classic CNNs also suffer from severe limitations such as computational inefficiency, overfitting on small datasets, and redundant feature extraction, which restrict their utility in clinical settings. To overcome these drawbacks, we introduce QAttn-CNN as a quantum-classical deep learning model combining a Quantum Attention Mechanism (QAttn) to improve feature selection and classification accuracy. Our method utilizes Quantum Convolutional Layers (QConv) and Quantum Image Representation (QIR) with Novel Enhanced Quantum Representation (NEQR) encoding to draw upon quantum parallelism and improve computational efficiency and complexity from O(N
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