The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images Two scenarios were considered to develop a system for predicting COVID-19 In the first scenario, the Gaussian filter was applied to remove noise from the chest X-ray radiograph images, and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs After segmentation, a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19 These features were processed using SVM and K-NN In the second scenario, a CNN transfer model (ResNet 50) was used to detect COVID-19 The system was examined and evaluated through multiclass statistical analysis, and the empirical results of the analysis found significant values of 97 14%, 99 34%, 99 26%, 99 26% and 99 40% for accuracy, specificity, sensitivity, recall and AUC, respectively Thus, the CNN model showed significant success;it achieved optimal accuracy, effectiveness and robustness for detecting COVID-19
Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm