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Deep Learning and Medical Imaging

Deep Learning and Medical Imaging
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Author(s): Nourhan Mohamed Zayed (Electronics Research Institute, Egypt) and Heba A. Elnemr (Electronics Research Institute, Egypt)
Copyright: 2019
Pages: 47
Source title: Intelligent Systems for Healthcare Management and Delivery
Source Author(s)/Editor(s): Nardjes Bouchemal (University Center of Mila, Algeria)
DOI: 10.4018/978-1-5225-7071-4.ch005

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Abstract

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

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