MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images

Abstract

COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.

Keywords: BGWO; BPSO; COVID-19; Deep learning models; Pneumonia.

Fig. 1

Dataset samples from original and enhancement data set.

Fig. 2

Support Vector Machine. * Default parameters of the Matlab program were used for SVM.

Fig. 3

Graphical abstract of MH-CovidNet.

Fig. 4

Confusion matrix for 2-class.

Fig. 5

Confusion matrix obtained from VGG19 for the original dataset.

Fig. 6

Training and validation accuracy of the models on the enhancement dataset.

Fig. 7

Confusion matrix obtained from VGG19 for the enhancement dataset.

Fig. 8

Confusion matrices with the method of 5-fold cross-validation for enhancement data.

Fig. 9

Confusion matrices obtained using the BPSO method.

Fig. 10

Confusion matrices obtained using the BPSO method; (a) by combining the features of the AlexNet model with the VGG19 model (30% test data) (b) by combining the features of the GoogleNet model with the ResNet model (30% test data).

Fig. 11

Confusion matrices obtained using the BGWO method.

Fig. 12

Confusion matrices obtained using the BGWO method; (a) by combining the features of the AlexNet model with the VGG19 model (k fold value = 5). (b) by combining the features of the GoogleNet model with the ResNet model (k fold value = 5).