Titlebook: Data-Driven Clinical Decision-Making Using Deep Learning in Imaging; M. F. Mridha,Nilanjan Dey Book 2024 The Editor(s) (if applicable) and

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https://doi.org/10.1007/978-3-322-99011-2er pathological abnormalities. Precise identification and categorization of kidney abnormalities using medical imaging methods is essential for precise diagnosis and efficient treatment planning in nephrology. This paper introduces an innovative deep-learning method for precisely categorising CT kid

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Verlag Chemie, Weinheim/Bergstr.ccessible breast cancer datasets like CBIS-DDSM. The primary objective is to produce a more varied set of images to enrich the training data for breast cancer detection systems by training a neural network to learn the inherent characteristics of mammogram images and generate new images which did no

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,H?rtebestimmungen an Sondermaterialien, novel approach to breast cancer segmentation by proposing an attention-enhanced U-Net architecture. The primary objective is to elevate the precision and efficiency of the segmentation process. Within our proposed architecture, attention blocks are seamlessly integrated into the U-Net framework, em

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978-981-97-3968-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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M. F. Mridha,Nilanjan DeyExplores cutting-edge medical imaging advancements and their applications in clinical decision-making.Addresses the development of multimodal machine learning models.Brings together a global network o