Diagnosis of Covid-19 disease using optimized convolutional neural networks

Document Type : Original Article

Authors

1 Faculty of Electrical and Computer Engineering, Razi University, Kermanshah, Iran.

2 Electrical Department-Electrical and Computer Faculty- Razi University-Kermanshah- Iran

Abstract

The covid-19 pandemic has caused many problems around the world for more than 2 years, so combating with this disease has become one of the important priorities of the human society to overcome these problems. One of the first steps to fight against this disease is the correct and quick diagnosis of this disease, and various methods have been proposed and presented to diagnose this disease. On the other hand, one of the most crucial tools to classification and recognition of images is the use of Convolutional Neural Networks. In this article, using convolutional neural networks, a structure is proposed that receives scanned images of people's lungs and after processing shows the person's status in 3 categories: Covid-19, healthy and pneumonia at the output. One of the most important advantages of this network refers to the high range of accuracy in comparison with other available ways. In addition, the suggested network in this article has been optimized with the use of grey wolf and genetic algorithms. As a result, its final structure has reached a suitable level in terms of complexity, while having much less the number of layers and the number of filters compared to previous networks, and has an acceptable level of accuracy compared to much wider networks. The suggested network has been trained by the Kaggle data set and finally, an accuracy of 95.34% has been obtained for it, which can be achieved higher accuracy if the amount of training data is increased.

Keywords

Main Subjects

References

[1] Calvo, C., López-Hortelano, M. G., de Carlos Vicente, J. C., Martínez, J. L. V., & de trabajo de la Asociación, G. (2020). Recommendations on the clinical management of the COVID-19 infection by the «new coronavirus» SARS-CoV2. Spanish Paediatric Association working group. Anales de Pediatría (English Edition), 92(4), 241-e1.‏
[2] Ciotti, M., Angeletti, S., Minieri, M., Giovannetti, M., Benvenuto, D., Pascarella, S., ... & Ciccozzi, M. (2019). COVID-19 outbreak: an overview. Chemotherapy, 64(5-6), 215-223.‏
[3] Aruleba, R. T., Adekiya, T. A., Ayawei, N., Obaido, G., Aruleba, K., Mienye, I. D., ... & Ogbuokiri, B. (2022). COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods. Bioengineering, 9(4), 153.‏
[4] Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems.
[5] Wang, Linda, Zhong Qiu Lin, and Alexander Wong. "Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images." Scientific Reports 10.1 (2020): 1-12.‏‏
[6] Ozturk, Tulin, et al. "Automated detection of COVID-19 cases using deep neural networks with X-ray images." Computers in biology and medicine 121 (2020): 103792.‏
[7] Murugan, R., Goel, T., Mirjalili, S., & Chakrabartty, D. K. (2021). WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images. Biocybernetics and Biomedical Engineering, 41(4), 1702-1718.‏
[8] Hassan, M. R., Ismail, W. N., Chowdhury, A., Hossain, S., Huda, S., & Hassan, M. M. (2022). A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19. The Journal of Supercomputing, 1-25.‏
[9] Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. arXiv preprint arXiv:2003.11597.‏
[10] Pathan, S., Siddalingaswamy, P. C., & Ali, T. (2021). Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture. Applied Soft Computing, 104, 107238.‏
[11] SIRMCOVID-19 Database (2020,May) Italian Society of Medical and Interventional Radiology COVID-19 dataset, Version 1. Retrieved may 05, 2020, from https://www.sirm.org/category/senza-categoria/covid-19/
[12] Rahman, COVID-19 Radiography Database, 2020, Retrieved October 02, 2020, https://www.kaggle.com/tawsifurrahman/covid19-radiographydatabase.
[13] Goel, T., Murugan, R., Mirjalili, S., & Chakrabartty, D. K. (2021). OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Applied Intelligence, 51(3), 1351-1366.‏
[14] Mikołajczyk, A., & Grochowski, M. (2018, May). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE.‏
[15] Wong, S. C., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016, November). Understanding data augmentation for classification: when to warp?. In 2016 international conference on digital image computing: techniques and applications (DICTA) (pp. 1-6). IEEE.‏
[16] Wang, W., Yang, Y., Wang, X., Wang, W., & Li, J. (2019). Development of convolutional neural network and its application in image classification: a survey. Optical Engineering, 58(4), 040901.‏
[17] Habib, G., & Qureshi, S. (2020). Optimization and acceleration of convolutional neural networks: A survey. Journal of King Saud University-Computer and Information Sciences.‏
[18] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
[19] Ali Akbar Alamdari, Abdollah Dosti Aref, Rabea Karimi Mahabadi, Zahra Rajabi, Nasrin Alamdari, Special Topics in Electrical and Computer Engineering with MATLAB, Tehran, Danesh, 2013
[20] Lambora, A., Gupta, K., & Chopra, K. (2019, February). Genetic algorithm-A literature review. In 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 380-384). IEEE.‏
[21] Vida Esmaeili, Mahmood Mohassel Feghhi, Diagnosis of Covid-19 Disease by Combining Hand-crafted and Deep-learning Methods on Ultrasound Data, Journal of Machine Vision and Image Processing, vol. 9, no. 4, pp. 31-41, 2022. https://dor.isc.ac/dor/20.1001.1.23831197.1401.9.4.3.0
[22] Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi, Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet, 9th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS), March 2-4, 2022.
‏

Files

Share

How to cite

Statistics