An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images

Shubashini Velu; Shubashini Velu

doi:10.3934/mbe.2023368

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 8400-8427. doi: 10.3934/mbe.2023368

Previous Article Next Article

Research article

An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images

Shubashini Velu ^,

Department of Management Information System, College of Business, Prince Mohammad Bin Fahd University, 617, Al Jawharah, Khobar, Dhahran, Saudi Arabia

Received: 25 September 2022 Revised: 01 January 2023 Accepted: 02 January 2023 Published: 02 March 2023

In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability.
- chest X-ray,
- deep learning,
- machine learning,
- MobileNetv2,
- SARS-COV-2,
- transfer learning
Citation: Shubashini Velu. An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 8400-8427. doi: 10.3934/mbe.2023368

Related Papers:

Abstract

In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability.

References

[1]	A. A. Abdelhamid, E. Abdelhalim, M. A. Mohamed, F. Khalifa, Multi-classification of chest X-rays for COVID-19 diagnosis using deep learning algorithms, Appl. Sci., 12 (2022), 2080. https://doi.org/10.3390/app12042080 doi: 10.3390/app12042080
[2]	W. S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., 5 (1943), 115–133.
[3]	Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: Analysis, applications and prospects, IEEE Trans. Neural Netw. Learn Syst., 12 (2022), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827 doi: 10.1109/TNNLS.2021.3084827
[4]	J. P. Cohen, L. Dao, K. Roth, P. Morrison, Y. Bengio, A. F. Abbasi, et al., Predicting COVID-19 pneumonia severity on chest X-ray with deep learning, Cureus, 12 (2020), e9448. https://doi.org/10.7759/cureus.9448 doi: 10.7759/cureus.9448
[5]	V. Ravi, H. Narasimhan, T. D. Pham, A cost‐sensitive deep learning‐based meta‐classifier for pediatric pneumonia classification using chest X‐rays, Expert Syst., (2020), e12966. https://doi.org/10.1111/exsy.12966 doi: 10.1111/exsy.12966
[6]	I. Borlea, R. Precup, A. Borlea, D. Iercan, A unified form of fuzzy C-means and K-means algorithms and its partitional implementation, Knowledge-Based Syst., 214 (2021), 106731. http://dx.doi.org/10.1016/j.knosys.2020.106731 doi: 10.1016/j.knosys.2020.106731
[7]	D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, A.Mittal, Pneumonia detection using CNN based feature extraction, in IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), (2019), 1–7.
[8]	M. Taresh, N. Zhu, T. A. A. Ali, Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks, Int. J. Biomed. Imaging, (2021), 8828404. https://doi.org/10.1155/2021/8828404 doi: 10.1155/2021/8828404
[9]	S. R. Velu, V. Ravi, K. Tabianan, Data mining in predicting liver patients using classification model, Health Technol. (Berl), 12 (2022), 1211–1235. https://doi.org/10.1007/s12553-022-00713-3 doi: 10.1007/s12553-022-00713-3
[10]	M. H. Alsharif, Y. H. Alsharif, K. Yahya, O. A. Alomari, M. A. Albreem, A. Jahid, Deep learning applications to combat the dissemination of COVID-19 disease: A review, Eur. Rev. Med. Pharmacol. Sci., 24 (2020), 11455–11460. https://doi.org/10.26355/eurrev_202011_23640 doi: 10.26355/eurrev_202011_23640
[11]	S. Sharma, Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: A study on 200 patients, Environ. Sci. Pollut. Res., 27 (2020), 37155–37163. https://doi.org/10.1007/s11356-020-10133-3 doi: 10.1007/s11356-020-10133-3
[12]	A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern Anal, Appl., 24 (2021), 1207–1220. https://doi.org/10.1007/s10044-021-00984-y doi: 10.1007/s10044-021-00984-y
[13]	H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, V. Singh, Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet, Chaos Solitons Fract., 138 (2020), 109944. https://doi.org/10.1016/j.chaos.2020.109944 doi: 10.1016/j.chaos.2020.109944
[14]	M. Singh, S. Bansal, S. Ahuja, R. K. Dubey, Panigrahi, B. K. Dey, Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data, Med. Biol. Eng. Comput., 59 (2021), 825–839. https://doi.org/10.1007/s11517-020-02299-2 doi: 10.1007/s11517-020-02299-2
[15]	A. M. Alqudah, S. Qazan, A. Alqudah, Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks, Emerg. Radiol., 4 (2020). https://doi.org/10.1007/s13246-020-00865-4 doi: 10.1007/s13246-020-00865-4
[16]	I. D. Apostolopoulos, T. A. Mpesiana, COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Eng. Sci. Med., 43 (2020), 635–640. https://doi.org/10.1016/j.eng.2020.04.010 doi: 10.1016/j.eng.2020.04.010
[17]	X. Xu, X.Jiang, C. Ma, P. Du, X. Li, S. Lv, et al., deep learning system to screen novel A Coronavirus Disease 2019 pneumonia, Engineering, 6 (2020), 1122–1129. https://doi.org/10.1016/j.eng.2020.04.010 doi: 10.1016/j.eng.2020.04.010
[18]	E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, M. Z. Parvez, CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images, Chaos Solitons Fract., 142 (2021), 110495. https://doi.org/10.1016/j.chaos.2020.110495 doi: 10.1016/j.chaos.2020.110495
[19]	S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, et al., A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19), Eur Radiol., 31 (2021), 6096–6104. https://doi.org/10.1007/s00330-021-07715-1 doi: 10.1007/s00330-021-07715-1
[20]	L. L, L. Qin, Z.Xu, Y. Yin, X. Wang, B. Kong, et al., Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT, Radiology, 296 (2020). https://doi.org/10.1148/radiol.2020200905 doi: 10.1148/radiol.2020200905
[21]	A. N. J Raj, H. Zhu, A. Khan, Z. Zhuang, Z. Yang, V. G. V. Mahesh, et al., ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans, PeerJ Comput. Sci., 7 (2021), e349. https://doi.org/10.7717/PEERJ-CS.349 doi: 10.7717/PEERJ-CS.349
[22]	H. Khalid, M. Hussain, M. A. Al Ghamdi, T. Khalid, K. Khalid, M. A. Khan, et al., A comparative systematic literature review on knee bone reports from MRI, X-rays and CT scans using deep learning and machine learning methodologies, Diagnostics, 10 (2020), 518. https://doi.org/10.3390/diagnostics10080518 doi: 10.3390/diagnostics10080518
[23]	G. Puneet, Pneumonia detection using convolutional neural networks, Int. J. Sci. Technol. Res., 7 (2021), 77–80. https://doi.org/10.46501/ijmtst070117 doi: 10.46501/ijmtst070117
[24]	X. Ding, Y. Guo, G. Ding, J. Han, Acnet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks, in IEEE/CVF international conference on computer vision (ICCV), (2019), pp. 1911–1920. http://dx.doi.org/10.1109/ICCV.2019.00200
[25]	S. Kostadinov, What is deep transfer learning and why is it becoming so popular? Towards Data Science, (2019).
[26]	M. Lascu, Deep learning in classification of Covid-19 coronavirus, pneumonia and healthy lungs on CXR and CT images, J. Med. Biol. Eng., 41 (2021), 514–522. http://dx.doi.org/10.1007/s40846-021-00630-2 doi: 10.1007/s40846-021-00630-2
[27]	X. Ma, B. Zheng, Y. Zhu, F. Yu, R. Zhang, B. Chen, Covid-19 lesion discrimination and localization network based on multi-receptive field attention module on CT images, Optik, 241 (2021), 167100. http://dx.doi.org/10.1016/j.ijleo.2021.167100 doi: 10.1016/j.ijleo.2021.167100
[28]	R. Kundu, R. Das, Z. W. Geem, G. T. Han, R. Sarkar, Pneumonia detection in chest X-ray images using an ensemble of deep learning models, PLoS One, 16 (2021), e0256630. https://doi.org/10.1371/journal.pone.0256630 doi: 10.1371/journal.pone.0256630

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)