HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification
This work addresses brain tumor diagnosis for medical imaging applications, but it is incremental as it applies a hybrid quantum-classical approach to a specific domain.
The paper tackled automated brain tumor classification from MRI images using a hybrid quantum-classical model, achieving 96.48% accuracy, which substantially outperformed a classical baseline at 86.72%.
We propose HQCM-EBTC, a hybrid quantum-classical model for automated brain tumor classification using MRI images. Trained on a dataset of 7,576 scans covering normal, meningioma, glioma, and pituitary classes, HQCM-EBTC integrates a 5-qubit, depth-2 quantum layer with 5 parallel circuits, optimized via AdamW and a composite loss blending cross-entropy and attention consistency. HQCM-EBTC achieves 96.48% accuracy, substantially outperforming the classical baseline (86.72%). It delivers higher precision and F1-scores, especially for glioma detection. t-SNE projections reveal enhanced feature separability in quantum space, and confusion matrices show lower misclassification. Attention map analysis (Jaccard Index) confirms more accurate and focused tumor localization at high-confidence thresholds. These results highlight the promise of quantum-enhanced models in medical imaging, advancing both diagnostic accuracy and interpretability for clinical brain tumor assessment.