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

Canayaz M., Şehribanoğlu S., Özdağ R., Demir M.

NEURAL COMPUTING & APPLICATIONS, cilt.34, sa.7, ss.5349-5365, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 7
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00521-022-07052-4
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.5349-5365
  • Anahtar Kelimeler: Coronavirus, Chest computed tomography, kNN, SVM, Bayesian Optimization, CLASSIFICATION, SELECTION
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

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.