Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks

TL;DR

This paper introduces a hybrid quantum convolutional neural network for brain tumor classification from MRI scans, achieving high accuracy and efficiency, and demonstrating potential for clinical application with near-term quantum hardware.

Contribution

The study presents a novel HQCNN architecture combining quantum encoding with separable convolutions, optimized for resource-limited quantum hardware and medical imaging tasks.

Findings

Achieved 99.16% training accuracy and 91.47% validation accuracy.

Reduced parameter count and circuit depth for hardware compatibility.

Demonstrated robust performance across varied imaging conditions.

Abstract

Accurate classification of brain tumors from MRI scans is critical for effective treatment planning. This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circuits with depth-wise separable convolutional layers to analyze images from a publicly available brain tumor dataset. Evaluated on this dataset, the HQCNN achieved 99.16% training accuracy and 91.47% validation accuracy, demonstrating robust performance across varied imaging conditions. The quantum layers capture complex, non-linear relationships, while separable convolutions ensure computational efficiency. By reducing both parameter count and circuit depth, the architecture is compatible with near-term quantum hardware and resource-constrained clinical environments. These results establish a foundation for integrating quantum-enhanced models into medical-imaging workflows with minimal changes to existing software platforms. Future work will extend evaluation to multi-center cohorts, assess real-time inference on quantum simulators and hardware, and explore integration with surgical-planning systems.