Название: Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and ApplicationsАвтор: Amit Kumar Tyagi
Издательство: Wiley-Scrivener
Год: 2021
Страниц: 528
Язык: английский
Формат: pdf (true)
Размер: 50.2 MB
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 Learning 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).
In the recent century, two concepts “Deep Learning” (DL) and “Blockchain technology” have received much attention from around the globe. Today’s DL (a superset of artificial neural networks, subset of Machine Learning (ML)) is being used rapidly in many sectors/fields, i.e., in methodological development and practical applications. DL offers many computational models which capture unfinished frameworks of massive data size, complementing majority of the hardware components, however it still faces a few challenges. Data analytics has gained extreme importance over the years and more data need to be analyzed to produce efficient results, leading to ML in HI. DL has proven to be a powerful tool under ML with many features and attributes. Similarly, DL is used in Medicare/bio-medical applications or biomedical informatics, i.e., clinical and HI. Hence, the chapter 5 discusses the use of DL for imaging, i.e., clinical and HI, with a systematic review/critical analysis of the relative merit, and potential pitfalls of the technique as well as its future prospects.
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