Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

Abstract

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.

[Retracted] Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

“…Usually, most of the worker bees stay in the hive for “in-house work,” and only a few of them search for food sources around the hive randomly. Once favorable honey gathering places or new high-quality honey sources are discovered, they will become honey gathering bees and fly back to the hive to tell the worker bees who are “back-office” in the hive about the location of food and the distance information between the hive and the honey source by dancing in circles or “ Figure 1 ” and also tell the type of food and the quality of honey source by the smell of pollen on their bodies, calling on everyone to gather honey [ 22 ]. The worker bees in the nest can not only judge the direction and distance of the nectar source by detecting the dance of the bees, but also evaluate the quality of the nectar source from the excitement of the dance [ 23 ].…”

COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers

“…By fitting a straight condition to a dependent variable, LR depicts the relationship between two variables [26][27][28]. One variable is treated as a dependent variable, while the other is treated as an independent variable.…”

A Deep Learning to Distinguish COVID-19 from Others Pneumonia Cases

“…Alsaade et al [10] have proposed a recognition system for classifying COVID-19 using chest X-ray/CT images. Three classification algorithms are considered for this research: SVM, KNN, and CNN.…”