Computational Analysis and Deep Learning for Medical Care by Amit Kumar Tyagi (Hardback)

£171.00
Out of stock
Product Ref. (e.g. ISBN, SKU)
9781119785729
Published:
24 Aug 2021
Format:
Hardback
Number of pages:
528 pages
Dimensions:
10x10x10mm
Weight:
0.454kgs
This text provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, the book covers 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 provides opportunities for several research communities including several research gaps in deep learning models (for healthcare applications).

The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems.

We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications.

Audience
Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.

About the Author: Amit Kumar Tyagi is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber physical systems and computer vision.

More Information
Number of pages 528

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