Abstract
An important task in combating COVID-19 involves the quick and correct diagnosis of patients, which is not only critical to the patient’s prognosis, but can also help to optimize the configuration of hospital resources. This work aims to classify chest radiographic images to help the diagnosis and prognosis of patients with COVID-19. In comparison to images of healthy lungs, chest images infected by COVID-19 present geometrical deformations, like the formation of filaments. Therefore, fractal dimension is applied here to characterize the levels of complexity of COVID-19 images. Moreover, real data often contains complex patterns beyond physical features. Complex networks are suitable tools for characterizing data patterns due to their ability to capture the spatial, topological and functional relationship between the data. Therefore, a complex network-based high-level data classification technique, capable of capturing data patterns, is modified and applied to chest radiographic image classification. Experimental results show that the proposed method can obtain high classification precision on X-ray images. Still in this work, a comparative study between the proposed method and the state-of-the-art classification techniques is also carried out. The results show that the performance of the proposed method is competitive. We hope that the present work generates relevant contributions to combat COVID-19.
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Acknowledgment
This work is supported in part by the Sao Paulo Research Foundation (FAPESP) under grant numbers 2015/50122-0, the Brazilian National Council for Scientific and Technological Development (CNPq) under grant number 303199/2019-9, and the Ministry of Science and Technology of China under grant number: G20200226015.
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Liu, W. et al. (2022). Analysis of Radiographic Images of Patients with COVID-19 Using Fractal Dimension and Complex Network-Based High-Level Classification. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_2
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