Perspective of AI system for COVID-19 detection using chest images: a review
| العنوان: | Perspective of AI system for COVID-19 detection using chest images: a review |
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| المؤلفون: | Dolly Das, Saroj Kumar Biswas, Sivaji Bandyopadhyay |
| المصدر: | Multimedia Tools and Applications. 81:21471-21501 |
| بيانات النشر: | Springer Science and Business Media LLC, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Computer Networks and Communications, Hardware and Architecture, Media Technology, Software |
| الوصف: | Coronavirus Disease 2019 (COVID-19) is an evolving communicable disease caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which has led to a global pandemic since December 2019. The virus has its origin from bat and is suspected to have transmitted to humans through zoonotic links. The disease shows dynamic symptoms, nature and reaction to the human body thereby challenging the world of medicine. Moreover, it has tremendous resemblance to viral pneumonia or Community Acquired Pneumonia (CAP). Reverse Transcription Polymerase Chain Reaction (RT-PCR) is performed for detection of COVID-19. Nevertheless, RT-PCR is not completely reliable and sometimes unavailable. Therefore, scientists and researchers have suggested analysis and examination of Computing Tomography (CT) scans and Chest X-Ray (CXR) images to identify the features of COVID-19 in patients having clinical manifestation of the disease, using expert systems deploying learning algorithms such as Machine Learning (ML) and Deep Learning (DL). The paper identifies and reviews various chest image features using the aforementioned imaging modalities for reliable and faster detection of COVID-19 than laboratory processes. The paper also reviews and compares the different aspects of ML and DL using chest images, for detection of COVID-19. |
| تدمد: | 1573-7721 1380-7501 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13ee9308f5253da2442ddafb942c90e7 https://doi.org/10.1007/s11042-022-11913-4 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....13ee9308f5253da2442ddafb942c90e7 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 15737721 13807501 |
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