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KSII Transactions on Internet and Information Systems

A Precise Classification of Medical Images with a Cost-Efficient Quantum-Classical Hybrid Neural Network

Tanvir Hasan, Hoon Ryu

Published May 31, 2026• 0 citations
Publisher
DOI
10.3837/tiis.2026.05.022

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This work discusses a cost-efficient hybrid quantum-classical neural network for classification of medical images. By embedding a 4-qubit variational quantum circuit (VQC) within a compact convolution neural network (CNN), the hybrid model achieves the accuracy of deep classical networks (99.89%) with much less parameters and faster training than ResNet-18 and VGG-16 model. Compared to a conventional CNN that shows the best training speed, the hybrid still remains even more parameter-efficient with similar speed, confirming its potential for real-time medical diagnostics in resource-constrained conditions.
Significant advances have been made in quantum computing, enabling researchers to create hybrid quantum- classical learning models for various tasks, including medical image classification. The classical approach to deep learning often faces challenges with limited data availability, high computational costs, and poor generalization. The hybrid Quantum- Classical Convolutional Neural Networks (HQCNNs) provide a new direction for overcoming the challenges faced by classical approaches. The hybrid approach enables the use of quantum circuits for feature encoding in higher-dimensional space, improving discrimination between patterns and reducing overfitting. In this paper, a hybrid quantum-classical convolutional neural network is developed for medical image classification. The hybrid approach is expected to capture non-linear relationships within medical image data while maintaining a lightweight network structure. The hybrid approach is found to have significant potential for efficient operation with limited data availability and noisy data. The classical approach is often outperformed by the hybrid approach. The focus of this paper is on architectural design and analysis of the developed hybrid quantum-classical convolutional neural network. The advantages of the developed hybrid quantum-classical convolutional neural network are de- lineated, including current challenges with quantum hardware.