Using ensemble deep learning to improve the accuracy of CT-Scan lung image detection of COVID-19 patients

Document Type : Original Article

Authors

Department of Computer Engineering, Khavaran Institute of Higher Education, Mashhad, Iran.

Abstract

Coronavirus disease 2019 or COVID-19 is an infectious disease caused by the severe acute respiratory syndrome virus (SARS-Cov-2) and has spread worldwide as an epidemic. After the rapid spread of the disease in 2019, the World Health Organization declared a public health emergency, and Human society saw a massive increase in deaths caused by its various mutations. Clinical symptoms include fever, cough, shortness of breath, and loss of smell. Fortunately, recently researchers have been able to achieve many successes in preventing the spread and speeding up its treatment by using different diagnostic methods. The aim of this research is to diagnose the disease of COVID-19 by processing CT scan images of people's lungs using ensemble deep learning techniques based on convolutional neural networks (CNN). In this regard, two data sets of CT scan images of people's lungs obtained from Kaggle and GitHub are used. The convolutional neural network architectures used in this research include VGG16, VGG19, Inception v3, ResNet50, DenseNet169, and CtNet10. In the first step, we added multiple dense (fully connected) layers to each of these models and evaluated their effect. Afterwards, to achieve higher accuracy and efficiency, the proposed ensemble method, which is a combination of VGG16, DenseNet169, and ResNet50 architectures, has been used. The experimental results show that the proposed ensemble method is able to achieve an accuracy of 98% to 100% on the investigated data set and significantly improve the performance of deep neural networks in multi-classification prediction tasks.

Keywords

Main Subjects


[1] W. Zhao, W. Jiang, and X. Qiu, “Deep learning for COVID-19 detection based on CT images,” Sci. Rep., vol.11, p. 14353, 2021, doi: 10.1038/s41598-021-93832-2.
[2] V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, and N. Mehendale, “Diagnosis of COVID-19 using CT scan images and deep learning techniques,” Emerg. Radiol., vol. 28, no. 3, pp. 497-505, 2021, doi: 10.1007/s10140-020-01886-y.‏
[3] H. Kaheel, A. Hussein, and A. Chehab, “AI-Based Image Processing for COVID-19 Detection in Chest CT Scan Images,” Front. Comms. Net, vol. 2, p. 645040, 2021, doi: 10.3389/frcmn.2021.645040.‏ ‏
[4] N. Safdarian and N. Jafarnia Dabanloo, “Detection and classification of COVID-19 by lungs computed tomography scan image processing using intelligence algorithm,” J. Med. Signals Sens., vol. 11, no.4, pp. 274-284, 2021, doi: 10.4103/jmss.JMSS_55_20.
[5] Silva P., E. Luz, G. Silva, G. Moreira, R. Silva, D. Lucio, and D. Menotti, “COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis,” Informatics Med. Unlocked, vol. 20, p. 100427, 2020, doi: 10.1016/j.imu.2020.100427.‏
[6] S. Tang, C. Wang, J. Nie, N. Kumar, Y. Zhang, Z. Xiong, and A. Barnawi, “EDL-COVID: ensemble deep learning for COVID-19 case detection from chest x-ray images,” IEEE Trans. Ind. Informatics, vol. 17, no. 9, pp. 6539-6549, 2021, doi: 10.1109/TII.2021.3057683.‏
[7] M. Polsinelli, L. Cinque, and G. Placidi, “A light CNN for detecting COVID-19 from CT scans of the chest,” Pattern Recognit. Lett., vol. 140, pp. 95-100, 2020, doi: 10.1016/j.patrec.2020.10.001.‏
[8] E. Soares, P. Angelov, S. Biaso, M.H. Froes, and D.K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” MedRxiv, pp. 1-8, 2020, doi: 10.1101/2020.04.24.20078584.‏
[9] G. Celik, “Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network,” Appl. Soft Comput., vol. 133, p. 109906, 2023, doi: 10.1016/j.asoc.2022.109906.‏
[10] K. Gupta and V. Bajaj, “Deep learning models-based CT-scan image classification for automated screening of COVID-19,” Biomed. Signal Process. Control, vol. 80, p. 104268, 2023, doi: 10.1016/j.bspc.2022.104268.‏ 
[11] D. Suganya and R. Kalpana, “Automated Detection of Covid-19 Waves with Computerized Tomography Scan Using Deep Learning,” in S. Awasthi, G. Sanyal, C.M. Travieso-Gonzalez, P. Kumar Srivastava, D.K. Singh, and R. Kant, (eds) Sustainable Computing, Springer, Cham, 2023, pp. 49-67, doi: 10.1007/978-3-031-13577-4_3.‏
[12] H. Allioui, Y. Mourdi, and M. Sadgal, “Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography,” Radiography, vol. 29, no. 1, pp. 109-118, 2023, doi: 10.1016/j.radi.2022.10.010.‏ 
[13] I. Ahmed, A. Chehri, and G. Jeon, “A Sustainable Deep Learning-Based Framework for Automated Segmentation of COVID-19 Infected Regions: Using U-Net with an Attention Mechanism and Boundary Loss Function,” Electronics, vol. 11, no. 15, p. 2296, 2022, doi: 10.3390/electronics11152296.‏
[14] D. Yang, C. Martinez, L. Visuna, H. Khandhar, C. Bhatt, and J. Carretero, “Detection and analysis of COVID-19 in medical images using deep learning techniques,” Sci. Rep., vol. 11, no. 1, p. 19638, 2021, doi: 10.1038/s41598-021-99015-3.‏ 
[15] E. Jangam, A.A.D. Barreto, and C.S.R. Annavarapu, “Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking,” Appl. Intell., vol. 52, no. 2, pp. 2243-2259, 2022, doi: 10.1007/s10489-021-02393-4.‏ 
[16] T. Choudhary, S. Gujar, A. Goswami, V. Mishra, and T. Badal, “Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification,” Appl. Intell., vol, 53, no. 6, pp. 7201-7215, 2023, doi: 10.1007/s10489-022-03893-7.‏‏
[17] S.V. Kogilavani, et al., “COVID-19 detection based on lung CT scan using deep learning techniques,” Computat. Math. Methods Med., vol. 2022, p. 7672196, 2022, doi: 10.1155/2022/7672196.‏ 
[18] V.K. Singh and M.H. Kolekar, “Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform,” Multimed. Tools Appl., vol. 81, no. 1, pp. 3-30, 2022, doi: 10.1007/s11042-021-11158-7.‏
[19] S. Serte and H. Demirel, “Deep learning for diagnosis of COVID-19 using 3D CT scans,” Comput. Biol. Med., vol. 132, p. 104306, 2021, doi: 10.1016/j.compbiomed.2021.104306.‏
[20] A. Akrami and M. Parsamanesh, “Investigation of a mathematical fuzzy epidemic model for the spread of coronavirus in a population,” Soft Comput. J., vol. 11, no. 1, pp. 2-9, 2022, doi: 10.22052/scj.2022.246053.1045 [In Persian].
[21] A. Yadollahi and H. Sabaghian-Bidgoli, “A simulation model for the propagation of Covid-19 virus based on the discrete-time Markov chain,” Soft Comput. J., vol. 11, no. 2, pp. 88-103, 2023, doi: 10.22052/scj.2023.246527.1076 [In Persian].
[22] M. Mousavi, S. Hosseini, and M.R. Omidi, “Improved deep neural network algorithm for COVID-19 detection in the Internet of Things,” Soft Comput. J., vol. 12, no. 2, pp. 54-71, 2024, doi: 10.22052/scj.2023.248686.1117 [In Persian].
[23] K.F. Haque and A. Abdelgawad, “A deep learning approach to detect COVID-19 patients from chest X-ray images,” AI, vol. 1, no. 3, p. 27, 2020, doi: 10.3390/ai1030027.‏
[24] J. Zhao, Y. Zhang, X. He, and P. Xie, “Covid-ct-dataset: a ct scan dataset about covid-19,” CoRR abs/2003.13865, 2020.
[25] A. Demir, F. Yilmaz, and O. Kose, “Early detection of skin cancer using deep learning architectures: resnet-101 and inception-v3,” in Med. Technol. Cong. (TIPTEKNO), Izmir, Turkey, 2019, pp. 1-4, doi: 10.1109/TIPTEKNO47231.2019.8972045.‏
[26] I.Z. Mukti and D. Biswas, “Transfer Learning Based Plant Diseases Detection Using ResNet50,” in 4th Int. Conf. Electr. Inform. Commun. Technol. (EICT), Khulna, Bangladesh, 2019, pp. 1-6, doi: 10.1109/EICT48899.2019.9068805.
[27] A. Vulli, et al., “Fine-tuned DenseNet169 for breast cancer metastasis prediction using FastAI and 1-cycle policy,” Sensors, vol. 22, no. 8, p. 2988, 2022, doi: 10.3390/s22082988.‏
[28] A.M. Javid, S. Das, M. Skoglund, and S. Chatterjee, “A ReLU Dense Layer to Improve the Performance of Neural Networks,” in IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Toronto, ON, Canada, 2021, pp. 2810-2814, doi: 10.1109/ICASSP39728.2021.9414269.‏