COVID-19 detection based on chest x-ray images using inception V3-BiLSTM

• Published in: AIP Conference Proceedings
• Main Author: Cahyani D.E.; Oktoviana L.T.; Hariadi A.D.; Setyawan F.F.; Setumin S.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184607086&doi=10.1063%2f5.0193859&partnerID=40&md5=bc94479b69c4abdddd99d1f13e675665

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. © 2024 Author(s).