Article: Review on machine and deep learning models for the detection and prediction of Coronavirus.
2020 Volume 33, Page(s) 3896–3901
Abstract: The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting millions of population and thousands of deaths worldwide. ... ...
| Abstract | The novel Coronavirus disease has increased rapidly in the Wuhan city of China in December 2019. This fatal virus has spread across the whole world like a fire in different stages and affecting millions of population and thousands of deaths worldwide. Therefore, it is essential to classify the infected people, so that they can take the precaution in the earlier stages. Also, due to the increasing cases spread of Coronavirus, there are only limited numbers of polymerase change reaction kits available in the hospitals for testing Coronavirus patients. That why it is extremely important to develop artificial intelligence-based automatic diagnostic tools to classify the Coronavirus outbreak. The objective of this paper is to know the novel disease epidemiology, major prevention from spreading of Coronavirus Severe Acute Respiratory Syndrome, and to assess the machine and deep learning-based architectures performance that is proposed in the present year for classification of Coronavirus images such as, X-Ray and computed tomography. Specifically, advanced deep learning-based algorithms known as the Convolutional neural network, which plays a great effect on extracting highly essential features, mostly in terms of medical images. This technique, with using CT and X-Ray image scans, has been adopted in most of the recently published articles on the Coronavirus with remarkable results. Furthermore, according to this paper, this can be noted and said that deep learning technology has potential clinical applications. |
|---|---|
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-06-23 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2797693-2 |
| ISSN | 2214-7853 |
| ISSN | 2214-7853 |
| DOI | 10.1016/j.matpr.2020.06.245 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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