IntroductionBreast cancer diagnosis in mammograms remains challenging due to limitations in preprocessing, accurate differentiation of benign and malignant cases, and precise tumor segmentation.MethodsWe 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.ResultsEvaluation 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.DiscussionThe proposed Quantum-SpinalNet provides a robust and interpretable framework for mammographic breast cancer detection, improving segmentation and classification precision, and supporting clinical diagnostic workflows.
Performance of large language models in delivering accurate and comprehensible patient information on heart failure and cardiomyopathy
BackgroundLarge language models (LLMs) are increasingly used by patients seeking cardiovascular health information through digital platforms. However, their accuracy and suitability for providing guidance on