Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language...

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Published in:	IEEE access Vol. 9; pp. 98523 - 98541
Main Authors:	Nisar, Dur-E-Maknoon, Amin, Rashid, Shah, Noor-Ul-Huda, Ghamdi, Mohammed A. Al, Almotiri, Sultan H., Alruily, Meshrif
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial intelligence Biological neural networks Brain modeling Cardiovascular system Central nervous system Computer vision Data processing Decision making Deep learning Diseases Health care Heart diseases Hidden Markov models Illnesses Machine learning Medical services Natural language processing nervous system Neural networks Respiratory system Solid modeling Speech recognition supervised and unsupervised learning
ISSN:	2169-3536, 2169-3536
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Abstract	In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.
AbstractList	In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.
Author	Alruily, Meshrif Nisar, Dur-E-Maknoon Amin, Rashid Ghamdi, Mohammed A. Al Shah, Noor-Ul-Huda Almotiri, Sultan H.
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SubjectTerms	Algorithms Artificial intelligence Biological neural networks Brain modeling Cardiovascular system Central nervous system Computer vision Data processing Decision making Deep learning Diseases Health care Heart diseases Hidden Markov models Illnesses Machine learning Medical services Natural language processing nervous system Neural networks Respiratory system Solid modeling Speech recognition supervised and unsupervised learning
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Title	Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities
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