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.
Artificial Intelligence; Chest X-Rays; COVID-19; CT scans; Deep Neural Networks; Computerized tomography; Diagnosis; Diseases; Image analysis; Medical imaging; Polymerase chain reaction; SARS; Chest radiographs; Chest X-ray; Chest X-ray image; Computerized tomography scan; CT-scan; Human errors; Imaging analysis; Learning models; Reverse transcription-polymerase chain reaction; Patient treatment
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
Similar
MEDLINE
...
LILACS
LIS