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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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發(fā)表于 2025-3-21 17:10:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging
編輯M. F. Mridha,Nilanjan Dey
視頻videohttp://file.papertrans.cn/285/284461/284461.mp4
概述Explores 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
叢書名稱Studies in Big Data
圖書封面Titlebook: Data-Driven Clinical Decision-Making Using Deep Learning in Imaging;  M. F. Mridha,Nilanjan Dey Book 2024 The Editor(s) (if applicable) and
描述.This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field‘s current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confiden
出版日期Book 2024
關(guān)鍵詞Medical Imaging; Breast Cancer; Deep Learning; Machine Learning; Convolutional Neural Network; Optimizati
版次1
doihttps://doi.org/10.1007/978-981-97-3966-0
isbn_softcover978-981-97-3968-4
isbn_ebook978-981-97-3966-0Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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,A Precise Cervical Cancer Classification in?the Early Stage Using Transfer Learning-Based Ensemble eatment, a principle applicable to all cancer variants. Although the Pap smear test stands as the benchmark for this type of cancer diagnosis, the accuracy of this diagnosis depends on the skill and attentiveness of the healthcare provider. Considerable efforts have been directed toward leveraging a
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,Unveiling Diagnostic Precision: Evaluating Machine Learning and?Deep Learning Approaches for?Pneumoion from large and complex medical image datasets. Currently, medical image datasets are increasing rapidly in size and complexity. Additionally, these algorithms are capable of processing and analyzing enormous amounts of data much more quickly and precisely than manual methods. However, it is chal
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Privacy-Preserving Vision-Based Detection of Pox Diseases Using Federated Learning,tection is vital for effective disease management and prevention. Traditional diagnostic methods often rely on invasive procedures and may lack privacy safeguards. In response, this research leverages advanced image analysis and federated learning to introduce a privacy-preserving framework for pox
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,Unveiling the?Unique Dermatological Signatures of?Human Pox Diseases Through Deep Transfer Learningies, potentially leading to misdiagnosis and delayed treatment. Currently, doctors look at samples by hand or rely on confirmation tests that are not always easy to obtain, such as polymerase chain reaction (PCR) tests, which take a long time. A few studies have focused on individual disease classif
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,Improved Classification of?Kidney Lesions in?CT Scans Using CNN with?Attention Layers: Achieving Hier 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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