COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches - Toğaçar, Mesut; Ergen, Burhan; Cömert, Zafer (Computers in biology and medicine, 2020)
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. [Display omitted] •Chest data obtained from patients infected with the new Coronavirus (COVID-19) were used.•It was detected with deep learning models using COVID-19, normal, and pneumonia chest data.•The original dataset was restructured with the Fuzzy Color technique and two datasets were stacked.•Efficient features were selected by applying Social Mimic optimization to feature sets extracted from CNN models.•The efficient features obtained were combined, and classified with a success rate of 99.27% with SVM method.