Classification of COVID-19 from chest X-ray images using a deep convolutional neural network (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The COVID-19 pandemic, also known as the coronavirus pandemic, is one of a major outbreak spreading across many countries around the world. It impacts severely on the health and life of many people all around the world. Medical imaging is a widely accepted technique for the early detection and diagnosis of disease that includes different techniques such as X-ray, computed tomography (CT) scan etc. For diagnosis COVID-19, chest X-ray is the imaging technique that plays an important role. In the recent years, deep neural networks have been successfully applied in many computer vision tasks including medical imaging. In this paper, we have experimented and evaluated DenseNet model for the classification of COVID-19 chest X-ray images. For that, a publicly available dataset contains 6432 chest X-ray images categorizes into 3 classes were used. Transfer learning and fine-tuning is applied for training the three variant of DenseNet model namely DenseNet121, DenseNet169 and DenseNet201. After evaluating the performance, it has been found that DenseNet201 achieved highest validation accuracy i.e. 0.9367 and lowest validation loss i.e. 0.1653 for classification of COVID-19 in chest X-ray images.