A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction

Article Sidebar

International Journal of Innovative Technology and Exploring Engineering (IJITEE)
PDF
Published: Aug 30, 2023
DOI: https://doi.org/10.35940/ijitee.I9696.0812923
Keywords:
COVID-19, X-Ray, Image, CNN, Categorization, Deep Learning

Main Article Content

Bhautik Daxini
Research Scholar, Department of Instrumentation and Control, Gujarat Technological University, Ahmedabad (Gujarat), India.
https://orcid.org/0009-0000-2760-3248
Dr. M.K. Shah
Associate Prof. & Head, Department of Instrumentation & Control Engineering, Vishwakarma Government Engineering College, Chandkheda, (Gujarat), India.
https://orcid.org/0009-0001-6167-0257
Rutvik K. Shukla
Assistant Prof., Department of Instrumentation & Control Engineering, Government Engineering College, Rajkot (Gujarat), India.
https://orcid.org/0000-0001-5223-8465
Dr. Rohit Thanki
Data Scientist, KRiAN GmbH, Wolfsburg, Germany.
https://orcid.org/0000-0002-0645-6266
Viral Thakar,
Senior Machine Learning Engineer, Autodesk, Toronto, Ontario, Canada.
https://orcid.org/0000-0002-6823-3906

Abstract

Deep learning methodologies are now feasible in practically every sphere of modern life because to technological advancements. Because of its high level of accuracy, deep learning can automatically diagnose and classify a wide variety of medical conditions in the field of medicine. The coronavirus first appeared in Wuhan, China, in December 2019, and quickly spread throughout the world. The pandemic of COVID-19 presented significant challenges to the world’s health care system. PCR and medical imaging can diagnose COVID-19. There has a negative impact on the health of people as well as the global economy, education, and social life. The most significant challenge in stymieing the rapid propagation of the disease is locating positive Corona patients as promptly as possible. Because there are no automated tool kits, additional diagnostic equipment will be required. According to radiological studies, these images include important information about the coronavirus. Accurate treatment of this virus and a solution to the problem of a lack of medical professionals in remote areas may be possible with the help of a specialized Artificial Intelligence (AI) system and radiographic pictures. We used pre-trained CNN models Xception, Inception, ResNet-50, ResNet-50V2, DenseNet121, and MobileNetV2 to correct the COVID-19 classification analytics. In this paper, we investigate COVID-19 detection methods that make use of chest X-rays. According to the findings of our research, the pre-trained CNN Model that makes use of MobileNetV2 performs better than other CNN techniques in terms of both the size of the solution and its speed. Our method might be of use to researchers in the process of fine-tuning the CNN model for efficient COVID screening.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
Bhautik Daxini, Dr. M.K. Shah, Rutvik K. Shukla, Dr. Rohit Thanki, and Viral Thakar, , Trans., “A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction”, IJITEE, vol. 12, no. 9, pp. 1–16, Aug. 2023, doi: 10.35940/ijitee.I9696.0812923.
Issue
Vol. 12 No. 9 (2023): Volume-12 Issue-9, August 2023
Section
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CC-BY-NC-ND 4.0

Author Biographies

Bhautik Daxini, Research Scholar, Department of Instrumentation and Control, Gujarat Technological University, Ahmedabad (Gujarat), India.

Bhautik Daxini is a research scholar in Instrumentation & Control Engineering Department of Gujarat Technological University, Ahmedabad, Gujarat. He has completed his M.Tech in Control System (2013) from Manipal Institute of Technology, Manipal, Karnataka. He has done his B.E. in Biomedical & Instrumentation (2008) from C.U. Shah College of Engineering & Technology, Surendranagar, Gujarat. He has received Gold Medal in his Master’s as well as in Bachelor’s Degree. He is at presently working as an Assistant Prof. in the Department of Instrumentation & Control at Shantilal Shah Govt. Engineering College, Bhavnagar. He has overall experience of about 10+ years in the academic field. His research interest include artificial intelligence, deep learning, medial image analysis, Control System and Biomedical Instrumentation.

Dr. M.K. Shah, Associate Prof. & Head, Department of Instrumentation & Control Engineering, Vishwakarma Government Engineering College, Chandkheda, (Gujarat), India.

Dr. M.K. Shah is an Associate Prof. and Head at Instrumentation & Control Engineering Dept. at VGEC – Chandkheda, Gujarat. He has completed his PhD (2016) from Gujarat Technological University and M.E.in Instrumentation and Control (2001) from Thapar University. He has completed his B.E. in Instrumentation & Control Engineering (1993) from L.D. Engineering College. He is having more than 29 years of teaching experience. He has received university gold medal for securing first rank in PG program at Thapar University. His research area includes artificial intelligence, deep learning, NIR spectroscopy, control and image processing. He has undertaken various responsibilities at various levels in his academic life.

Rutvik K. Shukla, Assistant Prof., Department of Instrumentation & Control Engineering, Government Engineering College, Rajkot (Gujarat), India.

Rutvik K. Shukla received his ME in Automatic control & Robotics from the Maharaja Sayajirav University (MSU), Baroda, Gujarat in 2009. In 2011 he was selected through public service commission in Government of Gujarat for the post of Assistant Professor in Vishwakarma Government Engineering college, chankheda, Ahmedabad, Gujarat. Later in the year 2019, he completed his PhD from Gujarat Technological University under the guidance of Dr. C. B. Bhatt. Some of his research area includes Biomedical engineering, Expert system, Ontology Engineering. He also has interest in the areas of deep learning, image processing and medical imaging. He 17 years of experience in the academic field.

Dr. Rohit Thanki, Data Scientist, KRiAN GmbH, Wolfsburg, Germany.

Dr. Rohit Thanki is an experienced computer vision expert and AI researcher with over 8 years of expertise in medical image analysis, biometrics, data security, and artificial intelligence. He has more than 3 years of academic experience in various engineering institutions in India and is currently working as a Data Scientist at KRiAN GmbH, Germany. Previously, he held the position of Head of Research & Development at Prognica Labs Tech FZCO, Dubai, UAE, and served as a consultant for Enno venture Technologies Private Limited, Bengaluru, India. Dr. Thanki holds a bachelor's degree in Electronics & Communication, a master’s degree in Communication Engineering, and a doctorate in Electronics & Communication with a specialization in digital image processing and data security. His research interests include medical image analysis, artificial intelligence, machine learning, deep learning, digital watermarking, data security, compressive sensing, and signal processing. He has authored over 40 publications in reputed journals with high impact factors and international conferences indexed in Web of Science and Scopus. Furthermore, Dr. Thanki has contributed to over 20 books with respected publishers such as Springer, CRC Press, Elsevier, De Gruyter, and IGI Global. He has also served as a reviewer for various reputable journals, including IEEE Transactions on Audio, Speech and Natural Language Processing, ACM Transactions on Multimedia Computing, Communications and Applications, IEEE Consumer Electronics Magazine, IEEE Access, IEEE Journal of Biomedical and Health Informatics, Signal Processing: Image Communication, Pattern Recognition, Computers and Electrical Engineering, Informatics in Medicine, Journal of Ambient Intelligence and Humanized Computing, IET Biometrics, and IET Image Processing, among others.

Viral Thakar,, Senior Machine Learning Engineer, Autodesk, Toronto, Ontario, Canada.

Viral Thakar is a highly skilled Machine Learning Research Engineer with expertise in computer vision, natural language processing, and IoT. He has a strong background in both research and applied engineering, with a focus on unsupervised domain adaptation, self-supervised learning, structured and unstructured as well as euclidean and non-euclidean data and one-shot learning. Viral has successfully contributed to the development of multidisciplinary AI-based solutions and has been recognized for his work in the field. With expertise in machine learning on edge devices and image and video signal processing, His interest area also includes, generative models, graph neural networks and convolutional neural networks. He did his M.Tech in Communication System Engineering (2013) from Charotar University of Science and Technology. He completed his B.E. Electronics and Communication (2011) from Atmiya Institute of Technology & Science.

How to Cite

[1]
Bhautik Daxini, Dr. M.K. Shah, Rutvik K. Shukla, Dr. Rohit Thanki, and Viral Thakar, , Trans., “A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction”, IJITEE, vol. 12, no. 9, pp. 1–16, Aug. 2023, doi: 10.35940/ijitee.I9696.0812923.
Share |
Crossref
Scopus
Google Scholar
Europe PMC

References

World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): situation report, 127. World Health Organization. https://apps.who.int/iris/handle/10665/332232

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognitive computation, 1–38. Advance online publication. https://doi.org/10.1007/s12559-022-10076-6 [CrossRef]

Chen Y, Liu Q, Guo D. Emerging coronaviruses: genome structure, replication, and pathogenesis. J Med Virol. 2020;92(4):418–23 [CrossRef]

Stoecklin SB, Rolland P, Silue Y, Mailles A, Campese C, Simondon A, et al. First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures. Eurosurveillance. 2020;25(6):2000094 [CrossRef]

Zhang N, Wang L, Deng K, Liang R, Su M, He C, Hu L, Su Y, Ren J, Yu F, Du L, Jiang S. Recent advances in the detection of respiratory virus infection in humans. J Med Virol. 2020. https:// doi. org/ 10. 1002/ jmv. 25674.

Muhammad LJ, Islam MM, Usman SS, Ayon SI. Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery. SN Comput Sci. 2020;1(4):206. https:// doi. org/ 10. 1007/ s42979- 020- 00216-w. [CrossRef]

Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, Bleicker T, Brünink S, Schneider J, Schmidt ML, Mulders DG, Haagmans BL, van der Veer B, van den Brink S, Wijsman L, Goderski G, Romette JL, Ellis J, Zambon M, Peiris M, Goossens H, Reusken C, Koopmans M, Drosten C. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro surveillance: bulletin Europeen sur les maladies transmissibles European communicable disease bulletin. 2020;25(3):2000045. https:// doi. org/ 10. 2807/ 1560- 7917. ES. 2020. 25.3. 20000 45. [CrossRef]

Lan L, Xu D, Ye G, Xia C, Wang S, Li Y, Xu H. Positive RTPCR test results in patients recovered from COVID-19. JAMA. 2020;323(15):1502–3. https:// doi. org/ 10. 1001/ jama. 2020. 2783. [CrossRef]

Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, Tan W. Detection of SARSCoV-2 in different types of clinical specimens. JAMA. 2020. https:// doi. org/ 10. 1001/ jama. 2020. 3786. [CrossRef]

Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N. Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humaniz Comput. 2021;1–9. https:// doi. org/ 10. 1007/ s12652- 020- 02623-6

ALzubi JA, Bharathikannan B, Tanwar S, Manikandan R, Khanna A, Thaventhiran C. Boosted neural network ensemble classification for lung cancer disease diagnosis. Appl Soft Comput. 2019;80:579–591. https:// doi. org/ 10. 1016/j. asoc. 2019. 04. 031. [CrossRef]

Li Y, Xia L. Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. AJR. 2020;214:1280–6. https:// doi. org/ 10. 2214/ AJR. 20. 22954 [CrossRef]

Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. Sensitivity of chest CT for COVID-19:comparison to RT-PCR. Radiology. 2020. https:// doi. org/ 10. 1148/ radiol. 20202 00432. [CrossRef]

Yang W, Sirajuddin A, Zhang X, Liu G, Teng Z, Zhao S, Lu M. The role of imaging in 2019 novel coronavirus pneumonia (COVID-19). Eur Radiol. 2020;30(9):4874–82. https:// doi. org/ 10. 1007/ s00330- 020- 06827-4 [CrossRef]

Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad ZA, Jacobi A, Li K, Li S, Shan H. CT Imaging features of 2019 novel coronavirus (2019-nCoV). Radiology. 2020;295(1):202–207. https:// doi. org/ 10. 1148/ radiol. 20202 00230. [CrossRef]

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, London, England. 2020;395(10223):497–506. https:// doi. org/ 10. 1016/ S0140- 6736(20) 30183-5. [CrossRef]

Islam AS, Islam MM. Diabetes prediction: a deep learning approach. Int J Inform Eng Electr Business. 2019;11(2):21–7. https:// doi. org/ 10. 5815/ ijieeb. 2019. 02. 03. [CrossRef]

Ayon SI, Islam MM, Hossain MR. Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J Res. 2020. https:// doi. org/ 10. 1080/ 03772 063. 2020. 17139 16 [CrossRef]

Islam MM, Iqbal H, Haque MR, Hasan MK. Prediction of breast cancer using support vector machine and K-Nearest neighbors. IEEE Region 10 Humanitarian Technology Conference. Dhaka, Bangladesh. 2017. https:// doi. org/ 10. 1109/ R10- HTC. 2017. 82889 44. [CrossRef]

Hasan MK, Islam MM, Hashem MMA. Mathematical model development to detect breast cancer using multigene genetic programming. 5th Int Conf Inform Electr Vision (ICIEV). Dhaka, Bangladesh. 2016. https:// doi. org/ 10. 1109/ ICIEV. 2016. 77600 68. [CrossRef]

Thevenot J, Lopez MB, Hadid A. A survey on computer vision for assistive medical diagnosis from faces. IEEE J Biomed Health Inform. 2020;22(5):1497–511. https:// doi. org/ 10. 1109/ JBHI. 2017. 27548 61. [CrossRef]

Rahaman A, Islam MM, Islam MR, Sadi MS, Nooruddin S. Developing IoT based smart health monitoring systems: a review.Rev Intell Artif. 2019;33:435–40. https:// doi. org/ 10. 18280/ ria. 330605. [CrossRef]

Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN Comput Sci. 2020;1:185. https:// doi. org/ 10. 1007/ s42979- 020- 00195-y. [CrossRef]

Jiang X. Feature extraction for image recognition and computer vision. Proc 2nd IEEE Int Conf Comput Sci Inf Techno ICCSIT. 2009. https:// doi. org/ 10. 1109/ ICCSIT. 2009. 52350 14. [CrossRef]

Kim TK, Yi PH, Hager GD, Lin CT. Refining dataset curation methods for deep learning-based automated tuberculosis screening. J Thoracic Dis. 2020;12(9):5078–5085. https:// doi. org/ 10. 21037/ jtd. 2019. 08. 34 [CrossRef]

Xia C, Li X, Wang X, Kong B, Chen Y, Yin Y, Cao K, Song Q, Lyu S, Wu X. A multi-modality network for cardiomyopathy death risk prediction with CMR images and clinical information. Med Image Comput Comput Assist Interv. 2019:577–585. https:// doi. org/ 10. 1007/ 978-3- 030- 32245-8_ 64. [CrossRef]

Yi PH, Kim TK, Lin CT. Generalizability of deep learning tuberculosis classifier to COVID-19 chest radiographs: new tricks for an old algorithm? J Thorac Imaging. 2020;35(4):W102–4. https:// doi. org/ 10. 1097/ RTI. 00000 00000 000532. [CrossRef]

Kermany DS, Goldbaum M, Cai W, Valentim C, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting M, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, et al. Identifying medical diagnoses and treatable diseases by imagebased deep learning. Cell. 2018;172(5):1122-1131.e9. https:// doi. org/ 10. 1016/j. cell. 2018. 02. 010 [CrossRef]

Depeursinge A, Chin AS, Leung AN, Terrone D, Bristow M, Rosen G, Rubin DL. Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high resolution computed tomography. Invest Radiol. 2015;50(4):261– 7. https:// doi. org/ 10. 1097/ RLI. 00000 00000 000127. [CrossRef]

Bhandare A, Bhide M, Gokhale P, Chandavarkar R. Applications of convolutional neural networks. Int J Comput Sci Inform Technol. 2016;7:2206–15.

Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10(3):257–73. https:// doi. org/ 10. 1007/ s12194- 017- 0406-5. [CrossRef]

Banihabib ME, Bandari R, Valipour M. Improving daily peak flow forecasts using hybrid Fourier-series autoregressive integrated moving average and recurrent artificial neural network models. AI. 2020;1(2):263–275. https:// doi. org/ 10. 3390/ ai102 0017. [CrossRef]

Soumya Ranjan Nayak, Deepak Ranjan Nayak, Utkarsh Sinha, Vaibhav Arora, Ram Bilas Pachori: Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study, Biomedical Signal Processing and Control, Volume 64, 2021, 102365, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2020.102365. [CrossRef]

Haque KF, Abdelgawad A. A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images. AI. 2020; 1(3):418-435. https://doi.org/10.3390/ai1030027 [CrossRef]

Moraru, Luminita, V J, Sharmila, D, Jemi Florinabel- 2021,: Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images, Computational and Mathematical Methods in Medicine, 2021, Volume 2021, Article ID 9269173, https://doi.org/10.1155/2021/9269173 [CrossRef]

Huang, Chaolin et al. “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.” Lancet (London, England) vol. 395,10223 (2020): 497-506. doi:10.1016/S0140-6736(20)30183-5 [CrossRef]

El Asnaoui, K., & Chawki, Y. (2021). Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of biomolecular structure & dynamics, 39(10), 3615–3626. https://doi.org/10.1080/07391102.2020.1767212 [CrossRef]

Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE reviews in biomedical engineering, 14, 4–15. https://doi.org/10.1109/RBME.2020.2987975. [CrossRef]

U Maheen, KI Malik, G Ali,: Comparative Analysis of Deep Learning Algorithms for Classification of COVID-19 X-Ray Images - arXiv preprint arXiv:2110.09294, 2021

Soares, Eduardo, Angelov, Plamen, Biaso, Sarah, Higa Froes, Michele, and Kanda Abe, Daniel. "SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification." medRxiv (2020). doi: https://doi.org/10.1101/2020.04.24.20078584. [CrossRef]