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Record Nr. |
UNINA9910483237803321 |
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Autore |
Kose Utku |
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Titolo |
Deep Learning for Medical Decision Support Systems / / by Utku Kose, Omer Deperlioglu, Jafar Alzubi, Bogdan Patrut |
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Pubbl/distr/stampa |
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Singapore : , : Springer 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 (xviii, 171 pages) : illustrations |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 909 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Health informatics |
Optical data processing |
Signal processing |
Image processing |
Speech processing systems |
Computational Intelligence |
Machine Learning |
Health Informatics |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Signal, Image and Speech 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 Innovative Medical Decision Support -- Deep Learning and Image Analysis for Medical Decision Support -- Deep Learning Oriented Systems for Medical Education -- Hybrid Deep Systems for Medical Education and Decision Support. |
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Sommario/riassunto |
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This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being |
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widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation. . |
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