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Neural Computing and Applications

Multi-classification of brain tumors using proposed hybrid quantum–classical integrated neural network (HQCINN) models: shallow and deep circuit approaches

Emine Akpinar, Sardar M. N. Islam, Murat Oduncuoğlu

Published Aug 13, 2025• 5 citations
Publisher
DOI
10.1007/s00521-025-11522-w

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Medical image classification is a critical task in computer-aided diagnostics, but classical deep learning models often demand substantial computational resources and large parameter spaces. This paper investigates the potential of hybrid quantum-classical neural networks to address this challenge in the context of multi-class brain tumor classification from MRI scans. We introduce a custom hybrid quantum machine learning (QML) model and conduct a rigorous comparative analysis against its purely classical counterpart, as well as established architectures such as ResNet-18 and VGG-16. Our findings demonstrate that the proposed hybrid QML model achieves a state-of-the-art test accuracy of 96.58%, matching the performance of the significantly larger ResNet-18 model. Crucially, the hybrid model outperforms its direct classical counterpart, the Classical Benchmark (96.20%), while utilizing approximately 80% fewer parameters. Although simulating quantum circuits results in longer training times, our findings highlight the remarkable parameter efficiency and competitive accuracy of hybrid quantum models, suggesting a promising direction for developing powerful yet lightweight networks for medical diagnostics.
This research work discusses three methods for detecting brain tumors using magnetic resonance imaging (MRI) - a pre-trained convolutional neural network (CNN) baseline, a pure quantum machine learning (QML) classifier that utilizes amplitude embedding and variational quantum circuits, and a hybrid of QML with CNN backbone model with a parameterised quantum circuit (PQC). All models were trained and tested on the four-class brain tumor MRI dataset (glioma, meningioma, pituitary, and no tumor) using the same preprocessing and evaluation protocols. The typical classical convolutional neural network (CNN) baseline achieved a test accuracy of 86.1%, with an F1 score of 0.8512. By comparison, the pure QML model provided a rather poor model, achieving limited accuracy of 44.7% and F1 of 0.4320, largely due to issues with shallow variational circuits and amplitude encoding on higher-dimensional data. The hybrid CNN-QML model provided the best results, achieving a test accuracy of 94.9% and an F1 of 0.9469, surpassing the typical classical baseline. These results indicate while standalone QML models are currently limited by the expressivity of quantum circuits and the scalability of their dimensionality, hybrid quantum-classical models can increase medical image classification performance, even when starting from a strong classical baseline.