Academic Journal
Deep learning models/techniques for COVID-19 detection: a survey
| العنوان: | Deep learning models/techniques for COVID-19 detection: a survey |
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| المؤلفون: | Archana, Kumari, Kaur, Amandeep, Gulzar, Yonis, Hamid, Yasir, Mir, Mohammad Shuaib, Soomro, Arjumand Bano |
| المساهمون: | King Faisal University |
| المصدر: | Frontiers in Applied Mathematics and Statistics ; volume 9 ; ISSN 2297-4687 |
| بيانات النشر: | Frontiers Media SA |
| سنة النشر: | 2023 |
| المجموعة: | Frontiers (Publisher - via CrossRef) |
| الوصف: | The early detection and preliminary diagnosis of COVID-19 play a crucial role in effectively managing the pandemic. Radiographic images have emerged as valuable tool in achieving this objective. Deep learning techniques, a subset of artificial intelligence, have been extensively employed for the processing and analysis of these radiographic images. Notably, their ability to identify and detect patterns within radiographic images can be extended beyond COVID-19 and can be applied to recognize patterns associated with other pandemics or diseases. This paper seeks to provide an overview of the deep learning techniques developed for detection of corona-virus (COVID-19) based on radiological data (X-Ray and CT images). It also sheds some information on the methods utilized for feature extraction and data preprocessing in this field. The purpose of this study is to make it easier for researchers to comprehend various deep learning techniques that are used to detect COVID-19 and to introduce or ensemble those approaches to prevent the spread of corona virus in future. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.3389/fams.2023.1303714 |
| DOI: | 10.3389/fams.2023.1303714/full |
| الاتاحة: | http://dx.doi.org/10.3389/fams.2023.1303714 https://www.frontiersin.org/articles/10.3389/fams.2023.1303714/full |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.D35B972D |
| قاعدة البيانات: | BASE |
| DOI: | 10.3389/fams.2023.1303714 |
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