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. 2022 Feb:72:103326.
doi: 10.1016/j.bspc.2021.103326. Epub 2021 Nov 9.

A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images

Amir Hossein Barshooi  1 , Abdollah Amirkhani  1
Affiliations

A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images

Amir Hossein Barshooi et al. Biomed Signal Process Control. 2022 Feb.

Abstract

A dangerous infectious disease of the current century, the COVID-19 has apparently originated in a city in China and turned into a widespread pandemic within a short time. In this paper, a novel method has been presented for improving the screening and classification of COVID-19 patients based on their chest X-Ray (CXR) images. This method eliminates the severe dependence of the deep learning models on large datasets and the deep features extracted from them. In this approach, we have not only resolved the data limitation problem by combining the traditional data augmentation techniques with the generative adversarial networks (GANs), but also have enabled a deeper extraction of features by applying different filter banks such as the Sobel, Laplacian of Gaussian (LoG) and the Gabor filters. To verify the satisfactory performance of the proposed approach, it was applied on several deep transfer models and the results in each step were compared with each other. For training the entire models, we used 4560 CXR images of various patients with the viral, bacterial, fungal, and other diseases; 360 of these images are in the COVID-19 category and the rest belong to the non-COVID-19 diseases. According to the results, the Gabor filter bank achieves the highest growth in the values of the defined evaluation criteria and in just 45 epochs, it is able to elevate the accuracy by up to 32%. We then applied the proposed model on the DenseNet-201 model and compared its performance in terms of the detection accuracy with the performances of 10 existing COVID-19 detection techniques. Our approach was able to achieve an accuracy of 98.5% in the two-class classification procedure; which makes it a state-of-the-art method for detecting the COVID-19.

Keywords: COVID-19; Classification; Data augmentation; Deep learning; Gabor; Generative adversarial network.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The steps and procedures of the proposed approach.
Fig. 2
Fig. 2
Sample modifications made to the traditional data augmentation process.
Fig. 3
Fig. 3
The structure of the GANs.
Fig. 4
Fig. 4
The arrangement of the layers in the generator and the discriminator models.
Fig. 5
Fig. 5
Examples of the unreal images produced by the GANs.
Fig. 6
Fig. 6
The outcome of applying the Sobel filter on a data; (a) the original data, (b) the data obtained after applying the Sobel filter.
Fig. 7
Fig. 7
The outcome of applying the LoG filter on a data; (a) the original data, (b) the data after applying the LoG filter.
Fig. 8
Fig. 8
The magnitudes (a) and the real parts (b) of the 64 filters used in our experiments in 8 scales and 8 orientations.
Fig. 9
Fig. 9
The convolution of magnitudes and real parts of 64 filters with the sample image.
Fig. 10
Fig. 10
The outcome of applying the Gabor filter.
Fig. 11
Fig. 11
The confusion matrixes for the test accuracy of the AlexNet, GoogleNet, VGG-19, ShuffleNet V2, DenseNet-121 and DenseNet-201 architectures (from top to down) for (a) Sobel, (b) LoG, and (c) the Gabor filter block.
Fig. 11
Fig. 11
The confusion matrixes for the test accuracy of the AlexNet, GoogleNet, VGG-19, ShuffleNet V2, DenseNet-121 and DenseNet-201 architectures (from top to down) for (a) Sobel, (b) LoG, and (c) the Gabor filter block.
Fig. 12
Fig. 12
Comparing the classification metrics of the proposed algorithm in 3 cases: (a) baseline (b) with GAN (c) GAN with Gabor filter.
See this image and copyright information in PMC

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