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Computational Analysis And Deep Learning For Medical Care Principles Methods And Applications 1st Edition Amit Kumar Tyagi Editor

  • SKU: BELL-33994738
Computational Analysis And Deep Learning For Medical Care Principles Methods And Applications 1st Edition Amit Kumar Tyagi Editor
$ 31.00 $ 45.00 (-31%)

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Computational Analysis And Deep Learning For Medical Care Principles Methods And Applications 1st Edition Amit Kumar Tyagi Editor instant download after payment.

Publisher: Wiley-Scrivener
File Extension: PDF
File size: 57.15 MB
Pages: 528
Author: Amit Kumar Tyagi (editor)
ISBN: 9781119785729, 1119785723
Language: English
Year: 2021
Edition: 1

Product desciption

Computational Analysis And Deep Learning For Medical Care Principles Methods And Applications 1st Edition Amit Kumar Tyagi Editor by Amit Kumar Tyagi (editor) 9781119785729, 1119785723 instant download after payment.

This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).

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