COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

• Published in: Neural computing & applications Vol. 34; no. 7; pp. 5349 - 5365
• Main Authors: Canayaz, Murat, Şehribanoğlu, Sanem, Özdağ, Recep, Demir, Murat
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Chest; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computed tomography; Computer Science; Coronaviruses; COVID-19; Data Mining and Knowledge Discovery; Diagnosis; Disease control; Image Processing and Computer Vision; Machine learning; Medical imaging; Optimization; Original; Original Article; Probability and Statistics in Computer Science; Viral diseases; Bayesian Optimization; kNN; Coronavirus; Chest computed tomography; SVM
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-022-07052-4

Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.