D3SENet: A hybrid deep feature extraction network for Covid-19 classification using chest X-ray images

Abstract

Covid-19 is one of the biggest global epidemics seen in the world in recent years. Because of this, people's daily lifestyles, the economic conditions of countries and individuals, and most importantly, their health status has been adversely affected all over the world. Millions of people around the world have died from this disease. For this reason, rapid and accurate detection of the disease is of great importance in terms of treatment and precautions. In addition, it is especially important to correctly distinguish between Covid-19 and non-Covid-19 pneumonia diseases for correct diagnosis and treatment. These two diseases cause similar symptoms, and the symptoms and the effects of the disease on the body should be carefully examined for their differentiation. Chest X-ray images, chest computerized tomography, and swab tests are commonly used to detect patients infected with COVID-19. This disease affects the lungs the most in the body and causes fatal side effects such as shortness of breath. Therefore, medical images taken from the chest play an important role in the diagnosis of the disease. The fact that X-rays are faster and cheaper than computerized tomography has led to an increase in studies on the detection of disease with X-rays.In recent years, the impressive results of deep learning in the field of computer vision have attracted researchers to this field when working with image data. This study aims to detect these diseases on chest X-ray images collected from Covid-19 patients, pneumonia patients, and healthy individuals. We proposed a hybrid feature extraction network namely D3SENET which consists of DarkNet53, DarkNet19, DenseNet201, SqueezeNet, and EfficientNetb0. After a balanced data set was prepared, feature vectors were obtained from images using deep learning-based CNN models and the size of feature vectors was reduced by feature selection algorithms. Obtained features were classified by traditional machine learning methods such as SVMs. The number of features to be selected was tested by the iterative increment method and the parameters with the highest accuracy rate were obtained. As a result, it was seen that healthy and infected individuals were detected in 3 classes with an accuracy rate of 98.78%. In addition, the confusion matrix, precision, recall values, and F1 score of the obtained model are also given.