Development of Website for COVID-19 Detection on Chest X-Ray Images

Control of the spread of COVID-19 must be encouraged, even though this is a new normal era. Rapid screening for COVID-19 detection must be carried out to control the spread of COVID-19. This research develops a website for COVID-19 detection based on chest X-Ray images and compares the CNN-BiLSTM model. This study divides X-ray images of the chest into three categories: COVID-19, Normal, and Viral Pneumonia. When compared to other models, the Resnet50-BiLSTM model produces the highest accuracy. The accuracy of the Resnet50-BiLSTM model was 98.51%. Then, in order, the following models were used: Resnet50, VGG19-BiLSTM, VGG19, AlexNet-BiLSTM, and AlexNet. The comparison of Precision, Recall, and F1-Measure findings also demonstrate that Resnet50-BiLSTM has the highest score when compared to other approaches. The website was also developed using the Flask framework for automatic COVID-19 detection. © 2022 IEEE.

COVID-19 classification using CNN-BiLSTM based on chest X-ray images

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

COVID-19 classification using CNN-BiLSTM based on chest X-ray images

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results. © 2023, Institute of Advanced Engineering and Science. All rights reserved.