Yabsera Erdaw1, Erdaw Tachbele2. 1. Electrical and Mechanical Engineering, Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. 2. Nursing & Midwifery, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
Abstract
PURPOSE: This research was designed to investigate the application of artificial intelligence (AI) in the rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) using digital chest X-ray images, and to develop a robust computer-aided application for the automatic classification of COVID-19 pneumonia from other pneumonia and normal images. MATERIALS AND METHODS: A total of 1100 chest X-ray images were randomly selected from three different open sources, containing 300 X-ray images of confirmed COVID-19 patients, 400 images of other pneumonia patients, and 400 normal X-ray images. In this study, a classical machine learning approach was employed. The model was built using the support vector machine (SVM) classifier algorithm. The SVM was trained by 630 features obtained from the HOG descriptor, which was quantized into 30 orientation bins in the range between 0 and 360. The model was validated using a 10-fold cross-validation method. The performance of the model was evaluated using appropriate classification metrics, including sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, kappa, and accuracy. RESULTS: The multi-level classification model was able to distinguish COVID-19 patients with a sensitivity of 97.92% and specificity of 98.91%, for the internal testing or cross-validation. For the independent external testing, the model showed sensitivity of 95% and specificity of 98.13%, for distinguishing COVID-19 from other pneumonia and no-findings. The binary classification model was able to distinguish COVID-19 patients with a sensitivity of 99.58% and specificity of 99.69%, for the internal testing. For the independent external testing, the model showed a sensitivity of 98.33% and specificity of 100%, for distinguishing COVID-19 from normal images. CONCLUSION: The model can achieve the rapid and accurate identification of COVID-19 patients from chest X-rays with more than 97% accuracy. This high accuracy and very rapid computer-aided diagnostic approach would be very helpful to control the pandemic.
PURPOSE: This research was designed to investigate the application of artificial intelligence (AI) in the rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) using digital chest X-ray images, and to develop a robust computer-aided application for the automatic classification of COVID-19 pneumonia from other pneumonia and normal images. MATERIALS AND METHODS: A total of 1100 chest X-ray images were randomly selected from three different open sources, containing 300 X-ray images of confirmed COVID-19 patients, 400 images of other pneumonia patients, and 400 normal X-ray images. In this study, a classical machine learning approach was employed. The model was built using the support vector machine (SVM) classifier algorithm. The SVM was trained by 630 features obtained from the HOG descriptor, which was quantized into 30 orientation bins in the range between 0 and 360. The model was validated using a 10-fold cross-validation method. The performance of the model was evaluated using appropriate classification metrics, including sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, kappa, and accuracy. RESULTS: The multi-level classification model was able to distinguish COVID-19 patients with a sensitivity of 97.92% and specificity of 98.91%, for the internal testing or cross-validation. For the independent external testing, the model showed sensitivity of 95% and specificity of 98.13%, for distinguishing COVID-19 from other pneumonia and no-findings. The binary classification model was able to distinguish COVID-19 patients with a sensitivity of 99.58% and specificity of 99.69%, for the internal testing. For the independent external testing, the model showed a sensitivity of 98.33% and specificity of 100%, for distinguishing COVID-19 from normal images. CONCLUSION: The model can achieve the rapid and accurate identification of COVID-19 patients from chest X-rays with more than 97% accuracy. This high accuracy and very rapid computer-aided diagnostic approach would be very helpful to control the pandemic.
Authors: Pranav Rajpurkar; Jeremy Irvin; Robyn L Ball; Kaylie Zhu; Brandon Yang; Hershel Mehta; Tony Duan; Daisy Ding; Aarti Bagul; Curtis P Langlotz; Bhavik N Patel; Kristen W Yeom; Katie Shpanskaya; Francis G Blankenberg; Jayne Seekins; Timothy J Amrhein; David A Mong; Safwan S Halabi; Evan J Zucker; Andrew Y Ng; Matthew P Lungren Journal: PLoS Med Date: 2018-11-20 Impact factor: 11.069