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AI-Driven Medical Imaging Analysis for COVID-19 Detection
2022 International Conference on Electronics and Renewable Systems, ICEARS 2022 ; : 1799-1804, 2022.
Article in English | Scopus | ID: covidwho-1831804
ABSTRACT
As of January 2019, there have been fears worldwide over COVID-19. In order to detect a person is affected by the virus is not, Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest X-Ray Images, Computerized Tomography (CT) scans are used. The patients who test positive for COVID-19 require early treatment and diagnosis. Manually analyzing the medical images of Chest radiographs and CT scans takes more time and are more susceptible to human error. So, to overcome this problem, Artificial Intelligence (AI) and Deep Learning-based tools are used to analyze medical images. This study focuses primarily on comparing deep learning models and finding the best one to detect COVID-19 in CT scans and X-rays of the chest. For X-Rays of the chest, COVID-19 Radiography Database is used, and SARS COV 2 Ct Scan Dataset is used for CT scans. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 International Conference on Electronics and Renewable Systems, ICEARS 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 International Conference on Electronics and Renewable Systems, ICEARS 2022 Year: 2022 Document Type: Article