Detection of Covid-19 from X-ray Images via Ensemble of Features Extraction Methods Employing Randomized Neural Networks

Yıl 2021, , 248 - 254, 30.12.2021

Ömer Faruk Ertuğrul , Emrullah Acar , Abdulkerim Öztekin , Erdoğan Aldemir

https://doi.org/10.36222/ejt.1035007

Cited By: 2

Öz

Artificial intelligence-based solutions have achieved significant successes in the field of health in recent years. These solutions have been started to be used for pre-diagnosis and decision support for a virus that spreads rapidly such as COVID-19 and thus creates fear and panic among the public. These solutions have augmented clinical expertise and thus have great potential to mitigate the virus outbreak burden of health experts. In this context, the load of healthcare workers can be significantly reduced through the help of an automatic diagnosis system of a high number of patients who apply to healthcare organizations with suspicion of disease. In this study, a machine-learning automatic diagnosis system exploiting x-ray images is proposed to detect diseases caused by COVID-19. The proposed system employs powerful texture features (Histogram of Oriented Gradients, Law’s Texture Energy Measure, Gabor Wavelet Transform, Gray Level Co-Occurrence Matrix, and local binary pattern) for the x-ray images to training a randomized neural network, a fast network, to establish a robust and fast diagnosis process for the virus. This study has raised the thesis that the mentioned image texture features extracted from the virus patients' images contain determinative indicators in two-dimensional space that make it possible to diagnose the disease. The proposed system contributes to the literature by using the tissue properties of x-ray images for the diagnosis of the virus. The disease is detected with an accuracy of 100 utilizing Law’s Texture Energy Measure feature and randomized neural network approach.

Anahtar Kelimeler

covid-19, neural network, textural features, automatic diagnosis

Kaynakça

• [1] S. B. Stoecklin et al., “First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020,” Eurosurveillance, vol. 25, no. 6, p. 2000094, 2020.
• [2] T. T. Team, “TrackCorona,” TrackCorona, 2021. .
• [3] Coronavirus Disease 2019, “Symptoms of Coronavirus,” 2021. .
• [4] T. P. Velavan and C. G. Meyer, “The COVID-19 epidemic,” Trop. Med. \& Int. Heal., vol. 25, no. 3, p. 278, 2020.
• [5] D. Flynn et al., “COVID-19 pandemic in the United Kingdom,” Heal. Policy Technol., vol. 9, no. 4, pp. 673–691, 2020.
• [6] CDC, “People Who Are at Higher Risk for Severe Illness.” .
• [7] M. E. H. Chowdhury et al., “Can AI help in screening viral and COVID-19 pneumonia?,” IEEE Access, vol. 8, pp. 132665–132676, 2020.
• [8] L. O. Hall, R. Paul, D. B. Goldgof, and G. M. Goldgof, “Finding covid-19 from chest x-rays using deep learning on a small dataset,” arXiv Prepr. arXiv2004.02060, 2020.
• [9] W. Wang et al., “Detection of SARS-CoV-2 in different types of clinical specimens,” Jama, vol. 323, no. 18, pp. 1843–1844, 2020.
• [10] M. Chung et al., “CT imaging features of 2019 novel coronavirus (2019-nCoV),” Radiology, vol. 295, no. 1, pp. 202–207, 2020.
• [11] Y. Fang et al., “Sensitivity of chest CT for COVID-19: comparison to RT-PCR,” Radiology, vol. 296, no. 2, pp. E115--E117, 2020.
• [12] O. Gozes et al., “Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection \& patient monitoring using deep learning ct image analysis,” arXiv Prepr. arXiv2003.05037, 2020.
• [13] F. Shi et al., “Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification (2020),” arXiv Prepr. arXiv2003.09860, 2003.
• [14] S. Wang et al., “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19),” Eur. Radiol., pp. 1–9, 2021.
• [15] Y. Li and L. Xia, “Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management,” Am. J. Roentgenol., vol. 214, no. 6, pp. 1280–1286, 2020.
• [16] J. Zhang et al., “Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection,” arXiv Prepr. arXiv2003.12338, 2020.
• [17] T. Cherian et al., “Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies,” Bull. World Health Organ., vol. 83, pp. 353–359, 2005.
• [18] G. Ortega et al., “Telemedicine, COVID-19, and disparities: policy implications,” Heal. policy Technol., vol. 9, no. 3, pp. 368–371, 2020.
• [19] kaggle, “No TitleChest X-ray Images (Pneumonia),” Chest X-ray Images (Pneumonia), 2021. .
• [20] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), 2005, vol. 1, pp. 886–893.
• [21] E. Acar and M. S. Ozerdem, “The texture feature extraction of Mardin agricultural field images by HOG algorithms and soil moisture estimation based on the image textures,” 2015, pp. 665–665, doi: 10.1109/siu.2015.7129912.
• [22] O. L. Junior, D. Delgado, V. Gonçalves, and U. Nunes, “Trainable classifier-fusion schemes: An application to pedestrian detection,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2009, pp. 432–437, doi: 10.1109/ITSC.2009.5309700.
• [23] I. Buciu and A. Gacsadi, “Gabor wavelet based features for medical image analysis and classification,” in 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2009, pp. 1–4.
• [24] M.-H. Horng and J.-H. Zhuang, “Texture feature coding method for texture classification,” Opt. Eng., vol. 42, no. 1, pp. 228–238, 2003.
• [25] A. Emrullah, “Extraction of texture features from local iris areas by GLCM and Iris recognition system based on KNN,” Eur. J. Tech., vol. 6, no. 1, pp. 44–52, 2016.
• [26] D. K. Iakovidis, D. E. Maroulis, and D. G. Bariamis, “FPGA architecture for fast parallel computation of co-occurrence matrices,” Microprocess. Microsyst., vol. 31, no. 2, pp. 160–165, 2007, doi: 10.1016/j.micpro.2006.02.013.
• [27] R. W. Conners, M. M. Trivedi, and C. A. Harlow, “Segmentation of a high-resolution urban scene using texture operators,” Comput. vision, Graph. image Process., vol. 25, no. 3, pp. 273–310, 1984.
• [28] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002, doi: 10.1109/TPAMI.2002.1017623.
• [29] R. Nosaka and K. Fukui, “HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns,” in Pattern Recognition, 2014, vol. 47, no. 7, pp. 2428–2436, doi: 10.1016/j.patcog.2013.09.018.
• [30] T. Chakraborti and A. Chatterjee, “A novel binary adaptive weight GSA based feature selection for face recognition using local gradient patterns, modified census transform, and local binary patterns,” Eng. Appl. Artif. Intell., vol. 33, pp. 80–90, 2014.
• [31] Ö. F. Ertu\ugrul and M. E. Ta\ugluk, “A fast feature selection approach based on extreme learning machine and coefficient of variation,” Turkish J. Electr. Eng. \& Comput. Sci., vol. 25, no. 4, pp. 3409–3420, 2017.
• [32] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
• [33] A. Öztekin and E. Erçelebi, “An efficient soft demapper for APSK signals using extreme learning machine,” Neural Comput. Appl., vol. 31, no. 10, pp. 5715–5727, 2019.
• [34] M. Li and D. Wang, “Insights into randomized algorithms for neural networks: Practical issues and common pitfalls,” Inf. Sci. (Ny)., vol. 382, pp. 170–178, 2017.
• [35] I. Castiglioni et al., “Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy,” Eur. Radiol. Exp., vol. 5, no. 1, pp. 1–10, 2021.
• [36] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput. Biol. Med., vol. 121, p. 103792, 2020.
• [37] M. Tougaçar, B. Ergen, and Z. Cömert, “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches,” Comput. Biol. Med., vol. 121, p. 103805, 2020.
• [38] X. He et al., “Sample-efficient deep learning for COVID-19 diagnosis based on CT scans,” medrxiv, 2020.
• [39] M. Fontanellaz et al., “A deep-learning diagnostic support system for the detection of COVID-19 using chest radiographs: a multireader validation study,” Invest. Radiol., vol. 56, no. 6, pp. 348–356, 2021.
• [40] Y. Wan, H. Zhou, and X. Zhang, “An interpretation architecture for deep learning models with the application of COVID-19 diagnosis,” Entropy, vol. 23, no. 2, p. 204, 2021.