Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection - Takeaways - MDSpire

Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection

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Quantum-SpinalNet is a hybrid deep learning model designed for improved breast cancer detection in mammograms.
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The model combines Swin ResUNet3+ for tumor segmentation with a Deep Quantum Neural Network and SpinalNet for classification.
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Evaluation on the CBIS-DDSM and DDSM datasets shows Quantum-SpinalNet achieves 93.8% accuracy and 94.1% sensitivity.
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The framework enhances tumor segmentation and classification precision, supporting clinical diagnostic workflows.
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Quantum-SpinalNet addresses challenges in mammogram analysis, including preprocessing and differentiation of tumor types.