Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models

• Main Authors: As'ari, Muhammad Amir, Ab. Manap, Nur Izzaty
• Format: Article
• Language: English
• Published: Universitas Ahmad Dahlan 2022
• Subjects: Q Science (General)
• Online Access: http://eprints.utm.my/id/eprint/100924/1/MuhammadAmirAs%27ari2022_Covid-19DetectionfromChestXRayImages.pdf; http://eprints.utm.my/id/eprint/100924/; http://dx.doi.org/10.26555/ijain.v8i2.807
• Institution: Universiti Teknologi Malaysia

Coronavirus disease 19 (Covid-19) is a pandemic disease that has already killed hundred thousands of people and infected millions more. At the climax disease Covid-19, this virus will lead to pneumonia and result in a fatality in extreme cases. COVID-19 provides radiological cues that can be easily detected using chest X-rays, which distinguishes it from other types of pneumonic disease. Recently, there are several studies using the CNN model only focused on developing binary classifier that classify between Covid-19 and normal chest X-ray. However, no previous studies have ever made a comparison between the performances of some of the established pre-trained CNN models that involving multi-classes including Covid-19, Pneumonia and Normal chest X-ray. Therefore, this study focused on formulating an automated system to detect Covid-19 from chest X-Ray images by four established and powerful CNN models AlexNet, GoogleNet, ResNet-18 and SqueezeNet and the performance of each of the models were compared. A total of 21,252 chest X-ray images from various sources were pre-processed and trained for the transfer learning-based classification task, which included Covid-19, bacterial pneumonia, viral pneumonia, and normal chest x-ray images. In conclusion, this study revealed that all models successfully classify Covid-19 and other pneumonia at an accuracy of more than 78.5%, and the test results revealed that GoogleNet outperforms other models for achieved accuracy of 91.0%, precision of 85.6%, sensitivity of 85.3%, and F1 score of 85.4%.