Automatic COVID-19 pneumonia diagnosis from x-ray lung image: A Deep Feature and Machine Learning Solution
Mohamed A. Ahmed, Z.T. Al-Qaysi, Moceheb Lazam Shuwandy, Mahmood M. Salih, Majid Hamid Ali
Abstract
Abstract Coronavirus disease 2019 was announced after unidentified pneumonia was discovered in Wuhan, China, and quickly spread around the world (COVID-19). This outbreak has claimed the lives of so many people. It has a long-term negative impact on public health. The goal of this study is to develop an intelligent computer-aided system that can detect positive COVID-19 cases automatically, which can help with daily medical problems. The proposed system is based on the convolution neural network (CNN) architecture and can automatically expose discriminative features on chest X-ray images due to its convolution with rich filter families and weight-sharing characteristics. As a deep feature extractor, the CNN model SqueezeNet was used. The extracted deep discriminative features were fed machine Decision Tree, Random Forest, Neural Network (NN), Naive Bayes, Logistic Regression, and k-nearest neighbor learning algorithms. As a result, the NN classifier with an accuracy of 97.24 per cent, a sensitivity of 0.9724, a specificity of 0.9858, and an F-score of 0.972 provided the most effective results. The high detection performance obtained in this study demonstrates the utility of deep CNN features and an NN classifier approach for detecting COVID-19 cases in CXR images. With the current resources, this would be hugely beneficial in speeding up disease diagnosis.