COVID-19 is an infectious disease whose first symptoms resemble the flu. The origin of the disease, which first started in China and rapidly spread to the rest of the world, is the SARS-CoV-2 (beta coronavirus) virus. Standard methods used for the diagnosis of COVID-19 are viral nucleic acid test and chest computed tomography imaging. The application process of these methods can take time. Elderly patients with chronic obstructive, cardiovascular, or hypertension are vulnerable to this condition, posing danger. For COVID-19 diagnosis, 47 models have been identified, 34 of which are based on medical illustrations. 16 prognostic models have been identified for determining the lethal state, the length of hospital stay, and disease progression.
Symptoms such as age, body temperature, blood pressure, and creatinine are taken into account most often when detecting COVID-19 . In the global fight against COVID-19, X-ray and computed tomography (CT) imaging tools play an important role. However, various picture characteristics can make differences in the interpretation of CT or X-ray scan results. The interpretation of CT or X-ray images by radiologists is moderate in the diagnosis of COVID-19. AI (Artificial Intelligence) technologies contribute to the power of imaging tools and assist experts. AI-supported image analysis simplifies workflow by providing minimal contact with the patient [2,3]. The combination of AI and imaging techniques can support the COVID-19 prognostic forecast. For this reason, AI-based systems are needed to improve performance in the diagnosis of COVID-19. In most medical imaging classifications, CNN is used. In some studies, SVM and RF were used. It is stated by the researchers that the proposed models perform well on the test data. This may not always be the case, since the classification success may change as a result of the noise situations on real-life data .
Ilyas et al. have observed ResNet, VGG19, InceptionV3 deep learning architectural results on chest x-ray images. VGG19, ResNet, ResNet50 and InceptionV3 achieve an accuracy of 98%, 96%, 95% and 96%, respectively . Diagnostic performance on Chest X-Ray (CXR) images may not be sufficient for routine clinical use. AI technologies are needed to improve the diagnostic performance of CXR. For this purpose, Oh et al. have applied U-Net, FC-DenseNet67, and FC-DenseNet103 architectures on CXR images. Accuracies of 85.9%, 81.8%, and 88.9% were achieved, respectively . Wang et al. applied the COVID-Net architecture on 13,975 CXR images taken from 13,870 patients. In COVID-19 detection, it gave more successful prediction results than VGG-19 and ResNet-50 models . Zhang et al. measured 83.61% AUC and 71.70% sensitivity on the X-VIRAL data set with the CAAD model . The X-VIRAL data set contains 5,977 viral pneumonia (no COVID-19), 18,619 non-viral pneumonia and 18,774 healthy CXR images . Farooq and et al. made COVID-19 detection on the COVIDx dataset containing CXR images. The results indicate an approximately 13% superior performance when compared to COVID-Net . Mahdy and et al. have used SVM for COVID-19 classification. The results serve 97.48% accuracy, 95.76% sensitivity, and 99.7% specificity . In another study, 89.2% accuracy was achieved on the 135 non-COVID-19 and 320 COVID-19 CXR images with the ResNet50 model . The COVID-CAPS model framework based on capsule networks has been presented on X-Ray images by Afhsar and et al. It was more successful than CNN based models. The model achieved 95.7% accuracy . In the study by Minaee et al., classification process was studied on 5000 Chest X-ray images with the help of popular convolutional neural networks. Model implementations were carried out with Pytorch. In the results, the specificity rate was around 90% and the sensitivity rate was around 97% . COVID-19 was detected with the DeTraC method proposed by Abbas and et al.. DeTrac has yielded effective results in classifying cases . In a study by Rajamaran et al. 99.01% accuracy, and 99.72% AUC was obtained with the deep learning model proposed on CXR images . In a study by Apostolopoulos et al., 96.78% accuracy was obtained in the diagnosis of COVID-19 disease on X-ray images . In a study by Singh et al., 98.94% accuracy was obtained with Xception architecture on X-ray pulmonary images .
Studies supporting CT images are also underway. Some of the CT findings of COVID-19 are consolidation, pleural thickening, and GGO. AI systems can assist doctors or radiologists to quickly diagnose the diseases. The AI system has been developed by Zhang et al to diagnose COVID-19 using CT scans. In the classification model, 361.221 CT images from 2246 patients were used for training. In the test phase, 40,880 images from 260 patients were used. The overall accuracy of the proposed model was around 92% . In another study on CT images, the AI model achieved 87% accuracy on independent test data . Ardakani et al. studied 1020 CT images (from 108 patients (COVID group) and 86 patients (non-COVID group)). For COVID-19 detection, AlexNet, VGGNet, GoogleNet, MobileNet, ResNet, SqueezeNet, and Xception architectures were used. The best results were obtained with ResNet-101 and Xception . In another study on CT images, a deep learning model was developed in 4352 images collected from 332 patients. AUC for COVID-19, pneumonia, and non-pneumonia has been obtained as 0.96, 0.95 and 0.98, respectively .
In this study, methods for the diagnosis of COVID-19 based on AI techniques are proposed. The proposed techniques have been evaluated on CXR scanning images. The general flow diagram of the study carried out for disease detection is given in Figure 1.