CNN-based Transfer Learning for Covid-19 Diagnosis

CNN-based transfer learning method plays a significant role in the detection of various objects such as cars, dogs, motorcycles, face and human detection in nighttime images by using visible light camera sensors. This method mainly depends on the images captured by cameras in order to detect the mentioned objects in a variety of environments based on convolutional neural networks (CNNs). In this study, we utilized the same method to detect coronavirus phenomena by using chest X-ray images that have been collected from three different open-source datasets with the aim of rapid detection of the infected patients and speed up the diagnostic process. We used one of the deep learning architectures in a Transfer Learning mode and modified its final layers to adapt to the number of classes in our investigation. The deep learning architecture that we used for the purpose of COVID-19 detection from X-ray images is a CNN designed to detect human in nighttime. We also modified the CNN architecture in three different scenarios named (Model 1, Model 2 and Model 3) in order to improve the classification results. Compared to model one and two, the result improved in model three and the number of misclassified cases reduced particularly in detecting Abnormal and COVID-19 cases. Although our CNN-based method shows high performance in COVID-19 detection, CNN decisions should not to be taken into consideration until clinical tests confirms symptoms of the infected patients.