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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Vydáno v:IEEE access Ročník 9; s. 98523 - 98541
Hlavní autoři: Nisar, Dur-E-Maknoon, Amin, Rashid, Shah, Noor-Ul-Huda, Ghamdi, Mohammed A. Al, Almotiri, Sultan H., Alruily, Meshrif
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3095312