Abstract
We propose a method by integrating image visibility graph and deep learning (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 Resnet34 CNN model 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 and 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.
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The authors declare that they have no known competing financial inter-est or personal relationships that could have appeared to influence the work reported in this paper.
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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.
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MPal conceived the idea and conceptualized it, developed the model concept and its code, performed the data pre-processing and image processing analysis, wrote and reviewed the manuscript. YT performed the CNN and MLP related code enhancement, analysis, model optimization and contributed to the manuscript writing. TVR developed primary model framework and code. PSRA debugged the code and performed CNN analysis. PKP conceptualized the idea of using haar wavelet with visibility graph, mentored the work and reviewed the manuscript.
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The data obtained for our analysis is from the available public domain database made for academic research purpose with approval from origi-nal researcher's institute ethical committee. Same approval is mentioned in their database DOI link from which we had obtained the data for our analysis in this work. We have obtained the data from two sources and appropriately cited the data reference in this work.
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Pal, M., Tiwari, Y., Reddy, T.V., Aditya, P.S.R., Panigrahi, P.K. (2024). An Integrative Method for COVID-19 Patients’ Classification from Chest X-ray Using Deep Learning Network with Image Visibility Graph as Feature Extractor. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_21
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