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Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection
Martina Jaincy D E1, Venkatasubbu Pattabiraman1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Introduction:
Breast cancer diagnosis in mammograms remains challenging due to limitations in preprocessing, accurate differentiation of benign and malignant cases, and precise tumor segmentation.
Methods:
We propose Quantum-SpinalNet, a hybrid deep learning model combining Swin ResUNet3+ for tumor segmentation with a Deep Quantum Neural Network (DQNN) and SpinalNet for classification. Preprocessing involves CEAMF-based denoising, Z-score normalization, and context-aware contrast enhancement using spatial energy curves. Swin ResUNet3+ integrates ResUnet3+ decoders with Swin Transformer encoders for effective tumor localization and context extraction.
Results:
Evaluation on the CBIS-DDSM and DDSM datasets demonstrates superior performance: accuracy 93.8%, sensitivity 94.1%, specificity 92.7%, precision 91.2%, F1 score 92.6%, Dice coefficient 0.89, and IoU 0.82.
Discussion:
The proposed Quantum-SpinalNet provides a robust and interpretable framework for mammographic breast cancer detection, improving segmentation and classification precision, and supporting clinical diagnostic workflows.