COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

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Toğaçar M., Ergen B., CÖMERT Z.

Computers in Biology and Medicine, cilt.121, 2020 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 121
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.compbiomed.2020.103805
  • Dergi Adı: Computers in Biology and Medicine
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Library, Information Science & Technology Abstracts (LISTA)
  • Anahtar Kelimeler: 2019-nCoV, COVID-19, Deep learning, Fuzzy color technique, Social mimic, Stacking technique
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Samsun Üniversitesi Adresli: Evet

Özet

Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.