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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2506.21937 |
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| _version_ | 1866916812705234944 |
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| author | Haddou, Marwan Ait Bennai, Mohamed |
| author_facet | Haddou, Marwan Ait Bennai, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification Haddou, Marwan Ait Bennai, Mohamed Machine Learning 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. |
| title | HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.21937 |