Matches in SemOpenAlex for { <https://semopenalex.org/work/W4304806871> ?p ?o ?g. }
- W4304806871 endingPage "19" @default.
- W4304806871 startingPage "1" @default.
- W4304806871 abstract "Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic." @default.
- W4304806871 created "2022-10-13" @default.
- W4304806871 creator A5005123741 @default.
- W4304806871 creator A5037586733 @default.
- W4304806871 creator A5047878382 @default.
- W4304806871 creator A5067954449 @default.
- W4304806871 creator A5080076757 @default.
- W4304806871 creator A5084823077 @default.
- W4304806871 creator A5089267840 @default.
- W4304806871 date "2022-10-12" @default.
- W4304806871 modified "2023-10-18" @default.
- W4304806871 title "Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey" @default.
- W4304806871 cites W1951063533 @default.
- W4304806871 cites W2097117768 @default.
- W4304806871 cites W2117538811 @default.
- W4304806871 cites W2884436604 @default.
- W4304806871 cites W2891784361 @default.
- W4304806871 cites W2901954625 @default.
- W4304806871 cites W2924911266 @default.
- W4304806871 cites W2945708832 @default.
- W4304806871 cites W2962914239 @default.
- W4304806871 cites W2963163009 @default.
- W4304806871 cites W2963446712 @default.
- W4304806871 cites W3004780338 @default.
- W4304806871 cites W3008095816 @default.
- W4304806871 cites W3008209068 @default.
- W4304806871 cites W3009557552 @default.
- W4304806871 cites W3011149445 @default.
- W4304806871 cites W3011414569 @default.
- W4304806871 cites W3012538234 @default.
- W4304806871 cites W3012916860 @default.
- W4304806871 cites W3013277995 @default.
- W4304806871 cites W3013601031 @default.
- W4304806871 cites W3013633552 @default.
- W4304806871 cites W3014058247 @default.
- W4304806871 cites W3014361272 @default.
- W4304806871 cites W3014680059 @default.
- W4304806871 cites W3014846667 @default.
- W4304806871 cites W3014903039 @default.
- W4304806871 cites W3014984262 @default.
- W4304806871 cites W3015698531 @default.
- W4304806871 cites W3016019826 @default.
- W4304806871 cites W3016540417 @default.
- W4304806871 cites W3017117984 @default.
- W4304806871 cites W3017623177 @default.
- W4304806871 cites W3017644243 @default.
- W4304806871 cites W3017855299 @default.
- W4304806871 cites W3019449959 @default.
- W4304806871 cites W3019531985 @default.
- W4304806871 cites W3020653337 @default.
- W4304806871 cites W3021001507 @default.
- W4304806871 cites W3021622280 @default.
- W4304806871 cites W3022133958 @default.
- W4304806871 cites W3023180050 @default.
- W4304806871 cites W3023398824 @default.
- W4304806871 cites W3023594394 @default.
- W4304806871 cites W3023617420 @default.
- W4304806871 cites W3023618360 @default.
- W4304806871 cites W3023763613 @default.
- W4304806871 cites W3024647574 @default.
- W4304806871 cites W3025352604 @default.
- W4304806871 cites W3025948831 @default.
- W4304806871 cites W3026801894 @default.
- W4304806871 cites W3027682070 @default.
- W4304806871 cites W3028427008 @default.
- W4304806871 cites W3030585150 @default.
- W4304806871 cites W3030621456 @default.
- W4304806871 cites W3032017599 @default.
- W4304806871 cites W3032455979 @default.
- W4304806871 cites W3033320889 @default.
- W4304806871 cites W3033616466 @default.
- W4304806871 cites W3034613280 @default.
- W4304806871 cites W3036552116 @default.
- W4304806871 cites W3036638392 @default.
- W4304806871 cites W3037105245 @default.
- W4304806871 cites W3037163353 @default.
- W4304806871 cites W3037420717 @default.
- W4304806871 cites W3037538421 @default.
- W4304806871 cites W3038197756 @default.
- W4304806871 cites W3039033741 @default.
- W4304806871 cites W3039137888 @default.
- W4304806871 cites W3039556020 @default.
- W4304806871 cites W3039629996 @default.
- W4304806871 cites W3039828206 @default.
- W4304806871 cites W3040124377 @default.
- W4304806871 cites W3041041945 @default.
- W4304806871 cites W3042796256 @default.
- W4304806871 cites W3043371311 @default.
- W4304806871 cites W3045032099 @default.
- W4304806871 cites W3048670851 @default.
- W4304806871 cites W3082967155 @default.
- W4304806871 cites W3085360812 @default.
- W4304806871 cites W3087795675 @default.
- W4304806871 cites W3093324510 @default.
- W4304806871 cites W3096956107 @default.
- W4304806871 cites W3102469298 @default.
- W4304806871 cites W3104810384 @default.
- W4304806871 cites W3105081694 @default.