Detection of Novel Coronavirus from Chest X-Ray Radiograph Images via Automated Machine Learning and CAD4COVID

Abstract

Recently, Artificial Intelligence (AI) has been considered as a valuable tool to detect early COVID-19 (Cov-19) infections and to monitor the condition of the infected patients. Machine learning and deep learning is a subset of AI that uses neural network algorithms. Hence, this study aimed to explore the sensitivity of CoV-19 detection by using CAD4COVID program (Delft Imaging, Netherland), and to evaluate the accuracy of the classifier performance using Automated Machine Learning (Auto ML) algorithm. 70 chest X-ray (CXR) images were assessed and of that, 39, 20 and 11 patients receive low range (0-35), medium range (36-65) and high range (66-100) probability score, respectively. The sensitivity of AutoML detection was 0.99, with an accuracy of 0.83. In summary, the AutoML with the best optimizer may comparable to CAD4COVID in detection of Cov-19 in term of its accuracy and sensitivity.

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