ACADSTAFF UGM
| Title | : | COVID-19 Chest X-Ray Classification Using Convolutional Neural Network Architectures |
| Author | : |
DEVI NURTIYASARI (1) Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2) |
| Date | : | 26 2021 |
| Keyword | : | convolutional neural network, covid-19, neural network, pandemic, coronavirus disease convolutional neural network, covid-19, neural network, pandemic, coronavirus disease |
| Abstract | : | World Health Organizations declared that Coronavirus Disease 2019 (COVID-19) outbreak pandemic in March 2020. Countries around the world are stepping up effort to halt the spread of this pandemic. Some countries are scrambling to tackle this virus by applying lockdown policy. As of 10 August 2020, there have been confirmed 19.718.030 total cases and 728.013 total deaths of COVID-19 [2]. COVID-19 detection is vital to decide the subsequent step in handling the patients. One strategy that may be applied for COVID-19 detection is classification approach primarily based totally on chest x-ray of the patients. Convolutional neural network has been successfully applied in practical applications. It is a type of machine learning which the model is designed to learn classification tasks directly from an image. It recognizes patterns directly from image pixel. These patterns are used to classify images and to eliminate the need of manual feature extraction. The classification provides outcomes with recall, precision, and accuracy had been respectively 94.99%, 95%, and 95.47% for model 1 and 97.73%, 95%, and 96.59% for model 2. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
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