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Record Nr. |
UNINA9910483238803321 |
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Titolo |
Deep Learning for Cancer Diagnosis / / edited by Utku Kose, Jafar Alzubi |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (XIX, 300 p. 118 illus., 87 illus. in color.) |
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Collana |
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Studies in Computational Intelligence, , 1860-9503 ; ; 908 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Cancer |
Medical informatics |
Image processing - Digital techniques |
Computer vision |
Signal processing |
Computational Intelligence |
Machine Learning |
Cancer Biology |
Health Informatics |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Signal, Speech and Image Processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Deep Learning for Enhancing Cancer Diagnosis -- Improved Deep Learning Techniques for Better Cancer Diagnosis -- Deep Learning for Diagnosing Rare Cancer Types -- Deep Learning for Histopathological Diagnosis -- Effective Use of Deep Learning and Image Processing for Cancer Diagnosis. |
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Sommario/riassunto |
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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 |
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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. |
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