Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913856201162752 |
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| author | Khan, Muhammad Al-Zafar Galib, Abdullah Al Omar Innan, Nouhaila Bennai, Mohamed |
| author_facet | Khan, Muhammad Al-Zafar Galib, Abdullah Al Omar Innan, Nouhaila Bennai, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks Khan, Muhammad Al-Zafar Galib, Abdullah Al Omar Innan, Nouhaila Bennai, Mohamed Image and Video Processing Quantum Physics 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. |
| title | Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks |
| topic | Image and Video Processing Quantum Physics |
| url | https://arxiv.org/abs/2401.15804 |