A Review on Image Classification of Medical Images for Tumor Detection Using Quantum Convolutional Neural Networks

Abstract

Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.

Introduction

Medical image analysis is critical for diagnosis, but classical CNNs face challenges with high-dimensional, noisy, and multi-modal medical data, along with heavy computational demands. Quantum computing offers potential speedups through superposition and entanglement, yet current NISQ devices are limited by noise and qubit counts. Recent advances in hybrid quantum–classical models and optimized circuits have made Quantum Convolutional Neural Networks (QCNNs) a promising alternative.

This work addresses the lack of scalable, symmetry-aware QCNNs for multi-modal medical imaging, particularly for tumor detection using MRI and CT scans. The proposed approach uses a distributed hybrid QCNN architecture to enhance feature extraction while minimizing qubit overhead. By combining classical preprocessing with shallow quantum circuits, the model aims to achieve improved accuracy and efficiency compared to classical CNNs, forming a foundation for quantum-enhanced AI in healthcare.

The literature review highlights that QCNNs and related quantum neural models often outperform classical methods in accuracy, convergence speed, and parameter efficiency on small-scale medical datasets. However, challenges remain in scalability, interpretability, security, noise resilience, and applicability to large, high-dimensional images.

The motivation stems from the growing volume and complexity of medical imaging data, where classical systems face computational bottlenecks. QCNNs theoretically offer advantages such as compact feature representation, improved pattern recognition via entanglement, and efficient handling of high-dimensional data. Nevertheless, practical deployment is constrained by encoding costs, decoherence, barren plateaus, and limited circuit depth.

The proposed methodology adopts a hybrid, distributed QCNN framework consisting of classical preprocessing, quantum convolutional layers with parameterized gates, measurement-based pooling, and classical fully connected layers for final classification. Circuit depth is kept shallow to suit NISQ hardware. Overall, the study positions QCNNs as a promising yet evolving approach for medical image classification, emphasizing the need for further research to bridge theoretical quantum advantages with real-world clinical applications.

Conclusion

Quantum Convolutional Neural Networks (QCNNs) have emerged as a promising field for quantum advantages in feature extraction and image classification, particularly for complex datasets like medical images like MRI, X ray, CT scan etc. The primary goal of the proposed research is based on Quantum Convolutional Neural Networks (QCNN) for medical image classification for Tumor detection to harness the power of quantum computing to significantly enhance the accuracy as well as efficiency of image recognition tasks with fewer resources. Expected Outcomes and Impact We anticipate 10-20% accuracy gains over traditional models on multi-modal tasks, with fewer parameters and polynomial speed-ups in training time. This could lead to edge-deployable quantum AI for telemedicine

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Copyright

Copyright © 2025 Priyanka Killedar , Dr. Vibha Tiwari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.