KCC 2021
Current Result Document :
| 한글제목(Korean Title) |
A Bayesian Optimized Model to Assist in Diagnosis of COVID-19 using Lung CT Images |
| 영문제목(English Title) |
A Bayesian Optimized Model to Assist in Diagnosis of COVID-19 using Lung CT Images |
| 저자(Author) |
Chu Myaet Thwal
Ye Lin Tun
Choong Seon Hong
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| 원문수록처(Citation) |
VOL 48 NO. 01 PP. 0642 ~ 0644 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With a high rate of spread, COVID-19 has become a global pandemic since 2020. As the early detection of coronavirus can increase the chance of survival among people, various methods are investigated besides the most popular diagnosis with RT-PCR test kits. Among them, lung screening through CT scan images is one of the effective ways to detect COVID-19 and control the outbreak in time. With the advancement of artificial intelligence and medical science, a Bayesian optimized neural architecture for the detection of coronavirus in lung CT images is proposed in this paper. The proposed system is an innovative system that helps in reducing the operational delay and burden for manual fine-tuning process of hyperparameters to obtain the best performing model. Moreover, it can be expected to provide accurate classification of medical images and assist the healthcare professionals in COVID-19 diagnosing. |
| 키워드(Keyword) |
Bayesian optimization
hyperparameter tuning
artificial neural network
CT scan images
COVID-19 detection
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| 파일첨부 |
PDF 다운로드
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