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International Scientific Journal of Engineering and Management

Hybrid Quantum-Classical Deep Learning for Chest X-ray Classification Using ResNet, MobileNet, and Quantum Models under Data-Constrained Conditions

Dr. C. Krishna Priya, Lankapalli Mahitha Chopra

Published May 6, 2026• 0 citations
Publisher

Abstract

Abstract Data scarcity and the computationally expensive nature of deep learning hinder its applications for medical image classification. This paper explored hybrid quantum-classical models for classifying chest X-rays in data-restricted environments. ResNet18, MobileNetV2, and its hybrid quantum variants were trained at 100%, 50%, 20% and 5% training sets respectively. The model was assessed by accuracy, F1-score, and the area under the curve (AUC). It was found that classical models had better accuracy compared to hybrid models, whereas hybrid models improved the AUC and hence class separability. In a low-data regime, MobileNet performed better due to its computationally lightweight nature, and the best performing Hybrid model combining efficiency with robustness is the MobileNet + Quantum model, revealing promising medical applications for the models where data is scarce. Keywords: Quantum Machine Learning, Deep Learning, MobileNet, ResNet, Chest X-ray, Medical Imaging

DOI
10.55041/isjem07217

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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 study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs’ application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs’ utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs’ robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques.