袖章
发表于 2025-3-28 18:04:08
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endure
发表于 2025-3-28 21:39:54
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清真寺
发表于 2025-3-29 01:24:13
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Agility
发表于 2025-3-29 06:51:52
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avenge
发表于 2025-3-29 10:40:08
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接合
发表于 2025-3-29 13:21:51
Privacy-Preserving Vision-Based Detection of Pox Diseases Using Federated Learning,tential of federated learning to revolutionize disease diagnosis while preserving individual confidentiality. This research contributes to enhancing disease management and underscores the significance of privacy-aware healthcare technologies.
发出眩目光芒
发表于 2025-3-29 16:37:27
,Unveiling the Unique Dermatological Signatures of Human Pox Diseases Through Deep Transfer Learningn human monkeypox, chickenpox, cowpox, measles, normal and hand-mouth face disease. Finally, our proposed model resulted in a test accuracy of 0.90, a precision of 0.89, a recall of 0.91, and an F1 score of 0.90, which significantly outperformed all other models, avoided common skin problems and exp
incarcerate
发表于 2025-3-29 20:53:06
,Improved Classification of Kidney Lesions in CT Scans Using CNN with Attention Layers: Achieving Hi exceptional performance. With a remarkable accuracy percentage of 97.98%, and average precision, detection, and F1 score of 98%. The accuracy and performance metrics attained demonstrate the efficacy and promise of the suggested method in aiding healthcare practitioners in the preliminary evaluatio
一加就喷出
发表于 2025-3-30 03:32:34
,Advancing Breast Cancer Diagnosis: Attention-Enhanced U-Net for Breast Cancer Segmentation,significant potential for augmenting accuracy and resilience in analyzing medical images related to various organs. This is achieved by providing a mechanism to assimilate specialized knowledge tailored to specific tasks within deep learning frameworks. Additionally, our comparative analysis against
阻挠
发表于 2025-3-30 05:46:06
,Privacy Preserving Breast Cancer Prediction with Mammography Images Using Federated Learning,ographic data may lead to precision medicine. The goal is to improve patient quality of life, reduce mortality, and enhance early detection. With a dataset of four classes and 6,649 images, the model achieves 72.46% accuracy, laying the foundation for advanced privacy-preserving risk prediction mode