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

Abstract

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.

Keywords: 2019-nCoV; COVID-19; Deep learning; Fuzzy color technique; Social mimic; Stacking technique.

Graphical abstract

Fig. 1

The sample images used in the experimental analysis of this study; (a) COVID-19 chest images, (b) normal chest images, (c) pneumonia chest images.

Fig. 2

The general design of the MobileNetV2 model used in this study [20].

Fig. 3

The general design of the SqueezeNet model used in this study [21].

Fig. 4

Design of SVM method for multiple classification process.$u=\overrightarrow{w}\cdot \overrightarrow{x}-b$$\frac{1}{2}{\Vert \overrightarrow{w}\Vert }^2$$y_i(\overrightarrow{w}\cdot {\overrightarrow{x}}_i-b)\ge 1,\forall i$

Fig. 5

Sub-data samples of the original dataset obtained by the Fuzzy Color technique.

Fig. 6

Sub-image samples obtained by the Stacking technique.

Fig. 7

The general design of the proposed approach.

Fig. 8

Training and test success graphs of the SqueezeNet model; (a) original dataset, (b) dataset restructured using the Fuzzy technique, (c) dataset combined using the Stacking technique.

Fig. 9

Confusion matrices of the SqueezeNet model; (a) original dataset, (b) dataset restructured using the Fuzzy technique, (c) dataset combined using the Stacking technique.

Fig. 10

Training and test success graphs of the MobileNetV2 model; (a) original dataset, (b) dataset restructured using the Fuzzy technique, (c) dataset combined using the Stacking technique.

Fig. 11

Confusion matrices of the MobileNetV2 model; (a) original dataset, (b) dataset restructured using the Fuzzy technique, (c) dataset combined using the Stacking technique.

Fig. 12

Confusion matrices with the method of 5-fold cross-validation of stacked data; (a) using the SqueezeNet model, (b) using the MobileNetV2 model.

Fig. 13

Confusion matrices obtained using the SMO method; (a) with the SqueezeNet model, (b) with the MobileNetV2 model, (c) with combining the features from the SqueezeNet model and the MobileNetV2 model (30% test data), (d) with combining the features from the SqueezeNet model and the MobileNetV2 model (k fold value = 5).