A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Majid Nour; Zafer Cömert; Kemal Polat

doi:10.1016/j.asoc.2020.106580

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Appl Soft Comput. 2020 Dec:97:106580. doi: 10.1016/j.asoc.2020.106580. Epub 2020 Jul 28.

Authors

Majid Nour¹, Zafer Cömert², Kemal Polat³

Affiliations

¹ Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
² Department of Software Engineering, Engineering Faculty, Samsun University, Samsun, Turkey.
³ Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey.

Abstract

A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.

Keywords: COVID-19; Deep feature extraction; Deep learning; Machine learning; Medical decision support system.