A Survey on Deep Learning in COVID-19 Diagnosis
According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/1/1 |
| _version_ | 1828057475230728192 |
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| author | Xue Han Zuojin Hu Shuihua Wang Yudong Zhang |
| author_facet | Xue Han Zuojin Hu Shuihua Wang Yudong Zhang |
| author_sort | Xue Han |
| collection | DOAJ |
| description | According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research. |
| first_indexed | 2024-04-10T21:13:14Z |
| format | Article |
| id | doaj.art-84ac206d07674713bb801e6c5d21729a |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-04-10T21:13:14Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-84ac206d07674713bb801e6c5d21729a2023-01-20T15:06:45ZengMDPI AGJournal of Imaging2313-433X2022-12-0191110.3390/jimaging9010001A Survey on Deep Learning in COVID-19 DiagnosisXue Han0Zuojin Hu1Shuihua Wang2Yudong Zhang3School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, ChinaSchool of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKAccording to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.https://www.mdpi.com/2313-433X/9/1/1COVID-19diagnosisdeep learningconvolutional neural networksCT imagestransfer learning |
| spellingShingle | Xue Han Zuojin Hu Shuihua Wang Yudong Zhang A Survey on Deep Learning in COVID-19 Diagnosis Journal of Imaging COVID-19 diagnosis deep learning convolutional neural networks CT images transfer learning |
| title | A Survey on Deep Learning in COVID-19 Diagnosis |
| title_full | A Survey on Deep Learning in COVID-19 Diagnosis |
| title_fullStr | A Survey on Deep Learning in COVID-19 Diagnosis |
| title_full_unstemmed | A Survey on Deep Learning in COVID-19 Diagnosis |
| title_short | A Survey on Deep Learning in COVID-19 Diagnosis |
| title_sort | survey on deep learning in covid 19 diagnosis |
| topic | COVID-19 diagnosis deep learning convolutional neural networks CT images transfer learning |
| url | https://www.mdpi.com/2313-433X/9/1/1 |
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