Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm

Authors

  • Xiaoyan Jiang Nanjing Normal University of Special Education
  • Mackenzie Brown Edith Cowan University image/svg+xml
  • Zuojin Hu Nanjing Normal University of Special Education
  • Hei-Ran Cheong University of Ulsan image/svg+xml

DOI:

https://doi.org/10.4108/eetel.v8i1.2344

Keywords:

Gray-level Cooccurrence Matrix, Genetic Algorithm, optimization, Feedforward Neural Network, K-fold cross-validation, COVID-19, Diagnosis

Abstract

Currently, improving the identification of COVID-19 with the help of computer vision and artificial intelligence has received great attention from researchers. This paper proposes a novel method for automatic detection of COVID-19 based on chest CT to help radiologists improve the speed and reliability of tests for diagnosing COVID-19. Our algorithm is a hybrid approach based on the Gray-level Cooccurrence Matrix and Genetic Algorithm. The Gray-level Cooccurrence Matrix (GLCM) was used to extract CT scan image features, GA algorithm was used as an optimizer, and a feedforward neural network was used as a classifier. Finally, we use 296 chest CT scan images to evaluate the detection performance of our proposed method. To more accurately evaluate the accuracy of the algorithm, 10-run 10-fold cross-validation was introduced. Experimental results show that our proposed method outperforms state-of-the-art methods in terms of Sensitivity, Accuracy, F1, MCC, and FMI.

References

Wubet, G.K., Value chain analysis of Garlic in LiboKemkem District: In the era of COVID-19, South Gondar Zone Amhara Region, Ethiopia. Cogent Business & Management, 2022. 9(1).

Klasen, J.M., et al., Medical students' perceptions of learning and working on the COVID-19 frontlines: ' horizontal ellipsis a confirmation that I am in the right place professionally'. Medical Education Online, 2022. 27(1).

Yang, L., EDNC: ensemble deep neural network for Covid-19 recognition. Tomography, 2022. 8(2): p. 869-890.

Zhang, X., Diagnosis of COVID-19 pneumonia via a novel deep learning architecture. Journal of Computer Science and Technology, 2022. 37(2): p. 330-343.

Jiang, F., et al., Review of the Clinical Characteristics of Coronavirus Disease 2019 (COVID-19). Journal of General Internal Medicine, 2020. 35(5): p. 1545-1549.

Xu, X.W., et al., Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: Retrospective case series. BMJ (online), 2020. 368: p. m606.

Chen, W., et al., A novel coronavirus outbreak of global health concern. Lancet (London, England). 395(10223): p. 470-473.

Shih, E., et al., Treatment of acute respiratory distress syndrome from COVID-19 with extracorporeal membrane oxygenation in obstetrical patients. American Journal of Obstetrics & Gynecology MFM, 2022. 4(2): p. 100537.

Yang, X., et al., Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, 2020. 8(5): p. 475-481.

Llitjos, J.F., et al., High incidence of venous thromboembolic events in anticoagulated severe COVID‐19 patients. Journal of Thrombosis and Haemostasis, 2020. 18.

Tianjin, C.A.o., W.D.G. Hospital, and W.T.D. Hospital, Handbook of Prevention and Treatment of the Pneumonia Caused by the Novel Coronavirus (2019-nCoV). 2020.

Wang, J., et al., CT characteristics of patients infected with 2019 novel coronavirus: association with clinical type. Clinical Radiology, 2020. 75(6): p. 408-414.

Dos Santos, C.F.G., et al., Gait Recognition Based on Deep Learning: A Survey. Acm Computing Surveys, 2023. 55(2).

Gafoor, S.A., et al., Deep learning model for detection of COVID-19 utilizing the chest X-ray images. Cogent Engineering, 2022. 9(1).

Lim, Y.W., A.S. Adler, and D.S. Johnson, Predicting antibody binders and generating synthetic antibodies using deep learning. Mabs, 2022. 14(1).

Hassan, H., et al., Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. Computer Methods and Programs in Biomedicine, 2022. 218: p. 106731.

Desai, S.B., A. Pareek, and M.P. Lungren, Deep learning and its role in COVID-19 medical imaging. Intelligence-Based Medicine, 2020. 3-4: p. 100013.

Aishwarya, T. and V.R. Kumar, Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review. SN Computer Science, 2021. 2(3).

Ameer, A.Q.A. and R.F. Mohammed, Covid-19 detection using CT scan based on gray level Co-Occurrence matrix. Materials Today: Proceedings, 2021.

Xu, L., R. Magar, and A. Barati Farimani, Forecasting COVID-19 new cases using deep learning methods. Computers in Biology and Medicine, 2022. 144: p. 105342.

Ahamed, K.U., et al., A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images. Computers in Biology and Medicine, 2021. 139: p. 105014.

Aggarwal, P., et al., COVID-19 image classification using deep learning: Advances, challenges and opportunities. Computers in Biology and Medicine, 2022. 144: p. 105350.

Lu, Z., A Pathological Brain Detection System Based on Radial Basis Function Neural Network. Journal of Medical Imaging and Health Informatics, 2016. 6(5): p. 1218-1222.

Yang, J., A pathological brain detection system based on kernel based ELM. Multimedia Tools and Applications, 2018. 77(3): p. 3715-3728.

Lu, S., A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm. CNS & Neurological Disorders - Drug Targets, 2017. 16(1): p. 23-29.

Wang, S., et al., Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization. International Journal of Computational Intelligence Systems, 2020. 13(1): p. 1332-1344.

Pi, P. and D. Lima, Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis. International Journal of Cognitive Computing in Engineering, 2021. 2: p. 93-103.

Acosta-González, E., J. Andrada-Félix, and F. Fernández-Rodríguez, On the evolution of the COVID-19 epidemiological parameters using only the series of deceased. A study of the Spanish outbreak using Genetic Algorithms. Mathematics and Computers in Simulation, 2022. 197: p. 91-104.

Santoni, M.M., et al., Cattle Race Classification Using Gray Level Co-occurrence Matrix Convolutional Neural Networks. Procedia Computer Science, 2015. 59: p. 493-502.

Srivastava, D., et al., Pattern-based image retrieval using GLCM. Neural Computing and Applications, 2020. 32(8): p. 1-14.

Akkoç, B., A. Arslan, and H. Kök, Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images. Computers in Biology and Medicine, 2016. 73: p. 102-107.

Ossai, C.I. and N. Wickramasinghe, GLCM and statistical features extraction technique with Extra-Tree Classifier in Macular Oedema risk diagnosis. Biomedical Signal Processing and Control, 2022. 73: p. 103471.

Li, D., et al., Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization. Underground Space, 2022.

Satapathy, S.C., Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder. Expert Systems, 2022. 39(3).

Satapathy, S.C., Secondary pulmonary tuberculosis identification via pseudo-Zernike moment and deep stacked sparse autoencoder. Journal of Grid Computing, 2022. 20.

Karaca, Y., Secondary pulmonary tuberculosis recognition by rotation angle vector grid-based fractional Fourier entropy. Fractals, 2022. 30(1).

Govindaraj, V., Deep Rank-Based Average Pooling Network for Covid-19 Recognition. Computers, Materials & Continua, 2022. 70: p. 2797-2813.

Khan, M.A., VISPNN: VGG-Inspired Stochastic Pooling Neural Network. Computers, Materials & Continua, 2022. 70: p. 3081-3097.

Wang, S.-H., DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy. ACM Transactions on Management Information Systems, 2021. 13(1).

Estran, R., A. Souchaud, and D. Abitbol, Using a genetic algorithm to optimize an expert credit rating model. Expert Systems with Applications, 2022. 203: p. 117506.

Alfarizi, M.G., M. Stanko, and T. Bikmukhametov, Well control optimization in waterflooding using genetic algorithm coupled with Artificial Neural Networks. Upstream Oil and Gas Technology, 2022. 9: p. 100071.

Li, J., Texture Analysis Method Based on Fractional Fourier Entropy and Fitness-scaling Adaptive Genetic Algorithm for Detecting Left-sided and Right-sided Sensorineural Hearing Loss. Fundamenta Informaticae, 2017. 151(1-4): p. 505-521.

Hou, X.-X., Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization. Computers and Electrical Engineering, 2017. 63: p. 126-138.

Singh, V.K., et al., Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity. Engineering Applications of Computational Fluid Mechanics, 2022. 16(1): p. 1082-1099.

Almuzaini, H.A. and A.M. Azmi, An unsupervised annotation of Arabic texts using multi-label topic modeling and genetic algorithm. Expert Systems with Applications, 2022. 203.

Boukhobza, A., et al., Design of orthogonal filter banks using a multi-objective genetic algorithm for a speech coding scheme. Alexandria Engineering Journal, 2022. 61(10): p. 7649-7657.

Kyriklidis, C., et al., Optimal Bio Marine Fuel production evolutionary Computation: Genetic algorithm approach for raw materials mixtures. Fuel, 2022. 323.

Khan, M.A., Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis. CMC-Computers, Materials & Continua, 2021. 69(3): p. 3145–3162.

Jiang, X., Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm. Frontiers in Neuroscience, 2021. 15(1098).

Zhang, Z. and X. Zhang, MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray. Pattern Recognition Letters, 2021. 150: p. 8-16.

Lyu, Z., et al., Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam. Materials, 2022. 15(4): p. 1477.

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Published

03-08-2022

How to Cite

[1]
X. Jiang, M. Brown, Z. Hu, and H.-R. Cheong, “Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm”, EAI Endorsed Trans e-Learn, vol. 8, no. 1, p. e2, Aug. 2022.