Abstract
We propose a method by integrating image visibility graph and deep neural network (DL) for classifying COVID-19 patients from their chest X-ray images. The computed assortative coefficient from each image horizonal visibility graph (IHVG) is utilized as a physical parameter feature extractor to improve the accuracy of our image classifier based on Resnet34 convolutional neural network (CNN). We choose the most optimized recently used CNN deep learning model, Resnet34 for training the pre-processed chest X-ray images of COVID-19 and healthy individuals. Independently, the preprocessed X-ray images are passed through a 2D Haar wavelet filter that decomposes the image up to 3 labels and returns the approximation coefficients of the image which is used to obtain the horizontal visibility graph for each X-ray image of both healthy and COVID-19 cases. The corresponding assortative coefficients are computed for each IHVG and was subsequently used in random forest classifier whose output is integrated with Resnet34 output in a multi-layer perceptron to obtain the final improved prediction accuracy. We employed a multilayer perceptron to integrate the feature predictor from image visibility graph with Resnet34 to obtain the final image classification result for our proposed method. Our analysis employed much larger chest X-ray image dataset compared to previous used work. It is demonstrated that compared to Resnet34 alone our integrative method shows negligible false negative conditions along with improved accuracy in the classification of COVID-19 patients. Use of visibility graph in this model enhances its ability to extract various qualitative and quantitative complex network features for each image. Enables the possibility of building disease network model from COVID-19 images which is mostly unexplored. Our proposed method is found to be very effective and accurate in disease classification from images and is computationally faster as compared to the use of multimode CNN deep learning models, reported in recent research works.
Significance An integrative method is proposed combining convolutional neural networks and 2D visibility graphs through a multilayer perceptron, for effective classification of COVID-19 patients from the chest x-ray images. In our study, the computed assortative coefficient from the horizontal visibility graph of each wavelet filtered X-ray image is used as a physical feature extractor. We demonstrate that compared to Resnet34 alone, our proposed integrative approach shows significant reduction in false negative conditions and higher accuracy in the classification of COVID-19 patients. The method is computationally faster and with the use of visibility graph, it also enables one to extract complex network based qualitative and quantitative parameters for each subject for additional understandings like disease network model building and its structures etc.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This research work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The author alone is responsible for the content and writing of the paper. The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
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The data obtained for our analysis is from the available public domain database made for academic research purpose and are appropriately cited in this work. The COVID-19 X-ray image database [35] is used to obtain 500 X-ray images of patients diagnosed with COVID-19. 500 healthy subjects Chest X-rays is obtained from the open-source database [36]. https://github.com/ieee8023/COVID-chestxray-dataset.
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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
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Data Availability
The COVID-19 X-ray image database [35] is used to obtain 500 X-ray images of patients diagnosed with COVID-19. 500 healthy subjects Chest X-rays is obtained from the open-source database [36].