TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach |
| 영문제목(English Title) |
A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach |
| 저자(Author) |
Dr. Ramya P
Dr. Venkatesh Babu S
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| 원문수록처(Citation) |
VOL 16 NO. 06 PP. 2018 ~ 2043 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms’ of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID and COVID-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%. |
| 키워드(Keyword) |
Adaptive Dragonfly Algorithm
Auto Augmentation
COVID-19 Predictor
Deep Learning
Ensemble Learning
Image Processing
UNet Segmentation
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| 파일첨부 |
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