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

By Mesut Toğaçar, B. Ergen, Zafer Cömert. Comput. Biol. Medicine, 2020

505 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7f9fe56592c2ba0ae37ac2182716667f7dfbda83

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In Computer Vision and Pattern Recognition

In Media Technology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, unknown): “Togacar et al. [57] developed MobileNet, a deep learning-based model, for COVID-19 detection.”
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review (2020, unknown): “Recent studies evident on the prospect of ML and AI technology for the various pandemic outbreak, it supports healthcare experts in various communicable diseases (SARS, EBOLA, HIV, COVID_19) [9-17] and non-communicable diseases (Cancer, Diabetic, Heart disease, and Stroke) [18-25] outbreak.”
A review on deep learning in medical image analysis (2021, methodology): “Regression is a problem of supervised learning involving predicting a numerical label [28].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “Then, given the speciﬁed states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”
Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network (2020, unknown): “Togacar, et al. [12] used SqueezeNet and MobileNetV2 to extract features, and employed social mimic optimization (SMO) for feature selection and combination.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, methodology): “Toğaçar, Ergen and Cömert [40] used a deep learning model to detect COVID-19 by X-ray images.”