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Titlebook: Deep Learning for Cancer Diagnosis; Utku Kose,Jafar Alzubi Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive lice

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發(fā)表于 2025-3-21 16:08:36 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Deep Learning for Cancer Diagnosis
編輯Utku Kose,Jafar Alzubi
視頻videohttp://file.papertrans.cn/265/264603/264603.mp4
概述Highlights recent advanced applications of Deep Learning for diagnosing cancer.Discusses relevant solutions for medical diagnosis using techniques such as CNN, LSTM, and Autoencoder Networks.Offers a
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Deep Learning for Cancer Diagnosis;  Utku Kose,Jafar Alzubi Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive lice
描述This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed..Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation..
出版日期Book 2021
關(guān)鍵詞Artificial intelligence; Machine learning; Medical diagnosis; Autoencoder Networks; Brain tumours; Histop
版次1
doihttps://doi.org/10.1007/978-981-15-6321-8
isbn_softcover978-981-15-6323-2
isbn_ebook978-981-15-6321-8Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
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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Ilia Bider,Paul Johannesson,Erik Perjonsgh resolution real histopathological microscope images has been developed. In order to determine the network performance, the well-known network models (VGG16, ResNET50, MobileNet-V2 and Inception-V3) were subjected to training and testing according to the same hardware and training criteria. While
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Designing Organizational Systems the core of tumor diagnosis. Pathology images provide clinical information about the tissues whereas the radiology images can be used for locating the lesions. This work aims at proposing a classification model which categorizes the tumor as oligodendroglioma (benign tumors) (or) astrocytoma (Malig
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J?rn Flohr Nielsen,Henrik Bendixen S?rensenld, advancements in software, hardware and precise and fine-tune images acquired from sensors. With the advancement in the field of medical and applications of Artificial Intelligence scaling to the height of improvement, modern state-of-the-art applications of Deep Learning for better cancer diagno
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