Covid CNN–SVM: Automated Categorization of Novel Coronavirus Disease from X-Ray Images

Abstract

In the computer-aided diagnostic (CAD) system, automated Coronavirus infection disclosure plays a crucial role in early identifying positive patients to prevent the disease from spreading further. The advent of algorithms for deep learning and machine learning has tackled classification tasks with promising results, especially in classifying images. However, the small size of the databases for medical images is a limitation associated with train deep neural networks. We use a combination of convolutional neural network (CNN) features and a support vector machine (SVM) for X-ray image classification to overcome this problem. This research work used CNN methods to extract features from 1,338 Chest X-ray frontal view image data. An SVM is used with CNN features to classify images in two classes: COVID-19 and Normal cases for enhanced performance. We conducted and evaluated our experiments on several public databases, which have been used in the recently published articles. The performance of the proposed method revealed accuracy, AUC, sensitivity, specificity of 0.995, 0.999, and 0.995 for classification, respectively. The high performance of the detection system achieved in this research reveals the effectiveness of deep features and the machine learning classifier approach for detecting COVID-19 cases using X-ray images. This would be extremely helpful in accelerating disease diagnosis with the available resources.