An Artificial Intelligence System for Detecting the Types of the Epidemic from X-rays : Artificial Intelligence System for Detecting the Types of the Epidemic from X-rays

Abstract

Since the beginning of the COVID-19 pandemic, many lives have been in danger. The visual geometry group network (VGGNet) is used in this research as a model to identify epidemic types. The dataset consisted of 1206S chest X-ray images extracted from the Kaggle website and evaluated in 4 classes: Pulmonary tuberculosis, normal lung, pneumonia, and Covid 19. We have used the VGGNet architecture to diagnose and classify the mentioned disease using the chest X-ray images. To assess the performance of these classes, the parameters such as accuracy, specificity, and sensitivity are measured. Regarding the measured parameters, the accuracy, specificity, and sensitivity values were 0.97, 0.96, and 0.98, respectively. This system can differentiate among these diseases by accurately diagnosing differences in patients’ X-ray images. The results showed that the VGG16 model could be more effective than VGG19 in diagnosing epidemics. The VGG16 based technique can facilitate the rapid diagnosis of patients and increase their chances of recovery. The findings also showed that the proposed model based on chest X-ray images is more accurate, simpler, and less expensive than computed tomography (CT) images.

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