| Autor: |
Cahyani, Denis Eka, Oktoviana, Lucky Tri, Hariadi, Anjar Dwi, Setyawan, Faisal Farris, Setumin, Samsul |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3049 Issue 1, p1-6, 6p |
| Abstrakt: |
Recently, the transmission of Coronavirus disease has not disappeared in the society. Rapid screening with high accuracy is needed to detect COVID-19 so that the virus does not spread more widely. Chest X-Ray (CXR) images may be utilized to detect COVID-19 infections. This research examines InceptionV3-BiLSTM to InceptionV3, Xception, and Xception-BiLSTM models for detecting COVID-19 using CXR. This research utilizes the Database of COVID-19 Radiographic Images, which includes three classes: COVID-19, Viral Pneumonia, and Normal. The data scenarios are divided into two types, namely scenario 1 containing original data and scenario 2 containing balanced data. The InceptionV3-BiLSTM model has the highest accuracy value in scenario 1 and scenario 2 data with accuracy values of 98.25% and 97.77%, respectively. Then the InceptionV3 model obtained the second-best accuracy value. Followed by the Xception-BiLSTM model and finally the Xception model in each data scenario. In all of the data scenarios, the InceptionV3-BiLSTM model has relatively higher precision, recall, and F1-measure values than the two competing models. So, the conclusion of this investigation is the InceptionV3-BiLSTM model can produce excellent outcomes utilizing CXR for COVID-19 detection. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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