tetano
Editor, Senior Moderator
Neural Comput Appl
. 2022 Feb 28;1-17.
doi: 10.1007/s00521-022-07052-4. Online ahead of print.
COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms
Murat Canayaz[SUP] 1 [/SUP], Sanem Şehribanoğlu[SUP] 2 [/SUP], Recep Özdağ[SUP] 1 [/SUP], Murat Demir[SUP] 3 [/SUP]
Affiliations
Abstract
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.
Keywords: Bayesian Optimization; Chest computed tomography; Coronavirus; SVM; kNN.
. 2022 Feb 28;1-17.
doi: 10.1007/s00521-022-07052-4. Online ahead of print.
COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms
Murat Canayaz[SUP] 1 [/SUP], Sanem Şehribanoğlu[SUP] 2 [/SUP], Recep Özdağ[SUP] 1 [/SUP], Murat Demir[SUP] 3 [/SUP]
Affiliations
- PMID: 35250180
- PMCID: PMC8884105
- DOI: 10.1007/s00521-022-07052-4
Abstract
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.
Keywords: Bayesian Optimization; Chest computed tomography; Coronavirus; SVM; kNN.