COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers

Coronavirus disease-2019 (COVID-19) is a serious infectious disease that is spreading rapidly all over the world. Scientists are looking for alternative diagnostic methods to detect and control the disease early. Artificial intelligence applications are promising in the COVID-19 epidemic. This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and differentiation from other viral pneumonia. The model we propose consists of three steps. In the first step, classification was made using the MobilenetV2, Efficientnetb0, and Darknet53 deep models. In the second step, the feature maps of the images in the Chest X-ray data set were extracted separately for each architecture using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. NCA method was preferred to reduce the size of these feature maps obtained. The feature maps obtained after dimension reduction were classified in the classic machine learning classifiers. In the third step, the feature maps obtained from each architecture were combined. After dimension reduction was applied to these combined features by applying the NCA method, this feature map is classified in the classifiers. The model we proposed was tested on two different data sets. The accuracy values obtained in these data sets are 99.05 and 97.1%, respectively. The obtained accuracy values show that the model is successful.

Automated summary

This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and distinguishing it from other viral pneumonia. The model achieved high accuracy in classifying the images and shows promising results in the diagnosis of COVID-19.