Joanna Albargi
ABSTRACT
The newly detected Coronavirus pneumonia, dubbed COVID-19, is highly contagious and pathogenic. To combat this disease, the diagnostic step is mostly carried out utilizing the RT-PCR technique on nasopharyngeal and throat samples with sensitivity values ranging from 30 to 70%. Biomedical imaging, on the other hand, has sensitivity levels of 98 and 69 percent, respectively. In this paper, a machine learning model is built using convolutional neural networks (CNN) with 5 CNN architectures: VGG16, MobileNetV2, NASNetMobile, and ResNet-50. The presented model scored a precision rate of 81%, a recall rate of 72%, and an f1-score of 71%. Moreover, this research paper accommodates a proposed expansion to the existing model. The Expansion suggested is to create a lightweight version of the model for smartphones
- [1] G. Gunčar , “An application of machine learning to haematological diagnosis,” Sci. Rep., vol. 8, no. 1, pp. 1–12, 2018, doi: 10.1038/s41598-017-18564-8.Google Scholar
Cross Ref
- [2] A. Alimadadi, S. Aryal, I. Manandhar, P. B. Munroe, B. Joe, and X. Cheng, “Artificial intelligence and machine learning to fight covid-19,” Physiol. Genomics, vol. 52, no. 4, pp. 200–202, 2020, doi: 10.1152/physiolgenomics.00029.2020.Google ScholarCross Ref
- [3] K. Y. Ngiam and I. W. Khor, “Big data and machine learning algorithms for health-care delivery,” Lancet Oncol., vol. 20, no. 5, pp. e262–e273, 2019, doi: 10.1016/S1470-2045(19)30149-4.Google ScholarCross Ref
- [4] J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, “COVID-19 open source data sets: a comprehensive survey,” Appl. Intell., vol. 51, no. 3, pp. 1296–1325, 2021, doi: 10.1007/s10489-020-01862-6.Google ScholarDigital Library
- [5] J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, “COVID-19 open source data sets: A comprehensive survey,” Appl. Intell., pp. 1296–1325, 2020, doi: 10.1101/2020.05.19.20107532.Google ScholarCross Ref
- [6] A. Shoeibi , “Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review,” 2020, [Online]. Available: http://arxiv.org/abs/2007.10785.Google Scholar
- [7] M. Poostchi, K. Silamut, R. J. Maude, S. Jaeger, and G. Thoma, “Image analysis and machine learning for detecting malaria,” Transl. Res., vol. 194, pp. 36–55, 2018, doi: 10.1016/j.trsl.2017.12.004.Google ScholarCross Ref
- [8] S. L. Goldenberg, G. Nir, and S. E. Salcudean, “A new era: artificial intelligence and machine learning in prostate cancer,” Nat. Rev. Urol., vol. 16, no. 7, pp. 391–403, 2019, doi: 10.1038/s41585-019-0193-3.Google ScholarCross Ref
- [9] S. Robertson, H. Azizpour, K. Smith, and J. Hartman, “Digital image analysis in breast pathology—from image processing techniques to artificial intelligence,” Transl. Res., vol. 194, pp. 19–35, 2018, doi: 10.1016/j.trsl.2017.10.010.Google ScholarCross Ref
- [10] J. Born , “POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS),” 2020, [Online]. Available: http://arxiv.org/abs/2004.12084.Google Scholar
- [11] S. Roy , “Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound,” IEEE Trans. Med. Imaging, vol. 39, no. 8, pp. 2676–2687, 2020, doi: 10.1109/TMI.2020.2994459.Google ScholarCross Ref
- [12] A. Zargari Khuzani, M. Heidari, and S. A. Shariati, “COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images,” Sci. Rep., vol. 11, no. 1, pp. 1–6, 2021, doi: 10.1038/s41598-021-88807-2.Google ScholarCross Ref
- [13] A. F. de M. Batista, J. L. Miraglia, T. H. R. Donato, and A. D. P. Chiavegatto Filho, “COVID-19 diagnosis prediction in emergency care patients: A machine learning approach,” medRxiv, vol. 102, 2020, doi: 10.1101/2020.04.04.20052092.Google ScholarCross Ref
- [14] J. Shen, Z. Shen, C. Xiong, C. Wang, K. Wang, and J. Han, “TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network,” Web Conf. 2020 - Proc. World Wide Web Conf. WWW 2020, pp. 486–497, 2020, doi: 10.1145/3366423.3380132.Google ScholarDigital Library
- [15] M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu, and A. T. Sahlol, “New machine learning method for imagebased diagnosis of COVID-19,” PLoS One, vol. 15, no. 6, 2020, doi: 10.1371/journal.pone.0235187.Google ScholarCross Ref
- [16] J. Rasheed, A. A. Hameed, C. Djeddi, A. Jamil, and F. Al-Turjman, “A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images,” Interdiscip. Sci. Comput. Life Sci., vol. 13, no. 1, pp. 103–117, 2021, doi: 10.1007/s12539-020-00403-6.Google ScholarCross Ref
- [17] J. Somasekar, P. Pavan Kumar, A. Sharma, and G. Ramesh, “Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities,” Mater. Today Proc., no. xxxx, pp. 3–6, 2020, doi: 10.1016/j.matpr.2020.09.352.Google ScholarCross Ref
- [18] M. Montazeri, R. ZahediNasab, A. Farahani, H. Mohseni, and F. Ghasemian, “Machine learning models for image-based diagnosis and prognosis of COVID-19: Systematic review,” JMIR Med. Informatics, vol. 9, no. 4, 2021, doi: 10.2196/25181.Google ScholarCross Ref
- [19] P. Saha, M. S. Sadi, and M. M. Islam, “EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers,” Informatics Med. Unlocked, vol. 22, p. 100505, 2021, doi: 10.1016/j.imu.2020.100505.Google ScholarCross Ref
- [20] H. Mohammad-Rahimi, M. Nadimi, A. Ghalyanchi-Langeroudi, M. Taheri, and S. Ghafouri-Fard, “Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review,” Front. Cardiovasc. Med., vol. 8, no. March, 2021, doi: 10.3389/fcvm.2021.638011.Google ScholarCross Ref
- [21] S. H. Kassania, P. H. Kassanib, M. J. Wesolowskic, K. A. Schneidera, and R. Detersa, “Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach,” Biocybern. Biomed. Eng., vol. 41, no. 3, pp. 867–879, 2021, doi: 10.1016/j.bbe.2021.05.013.Google ScholarCross Ref
- [22] Y. J. Cho , “Lung ultrasound for early diagnosis and severity assessment of pneumonia in patients with coronavirus disease 2019,” Korean J. Intern. Med., vol. 35, no. 4, pp. 771–781, 2020, doi: 10.3904/KJIM.2020.180.Google ScholarCross Ref
- [23] F. Sadik, A. G. Dastider, and S. A. Fattah, “SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos,” Heal. Inf. Sci. Syst., vol. 9, no. 1, pp. 1–11, 2021, doi: 10.1007/s13755-021-00154-8.Google ScholarCross Ref
- [24] G. Muhammad and M. Shamim Hossain, “COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images,” Inf. Fusion, vol. 72, no. October 2020, pp. 80–88, 2021, doi: 10.1016/j.inffus.2021.02.013.Google ScholarCross Ref
- [25] J. Diaz-Escobar , “Deep-learning based detection of COVID-19 using lung ultrasound imagery,” PLoS One, vol. 16, no. 8 August, pp. 1–21, 2021, doi: 10.1371/journal.pone.0255886.Google ScholarCross Ref
- [26] A. Ebadi, P. Xi, A. Maclean, S. Tremblay, S. Kohli, and A. Wong, “COVIDx-US - An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics,” pp. 1–12, 2021.Google Scholar
- [27] L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Sci. Rep., vol. 10, no. 1, pp. 1–12, 2020, doi: 10.1038/s41598-020-76550-z.Google ScholarCross Ref
- [28] A. T. Sahlol, D. Yousri, A. A. Ewees, M. A. A. Al-qaness, R. Damasevicius, and M. A. Elaziz, “COVID-19 image classification using deep features and fractional-order marine predators algorithm,” Sci. Rep., vol. 10, no. 1, pp. 1–15, 2020, doi: 10.1038/s41598-020-71294-2.Google ScholarCross Ref
- [29] Karen Simonyan∗ & Andrew Zisserman+, “VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION Karen,” Am. J. Heal. Pharm., vol. 75, no. 6, pp. 398–406, 2018.Google Scholar
- [30] Mark Sandler, A. Howard, M. Zhu, A. Zhmoginov, and Liang-Chieh Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark,” Convolutional Neural Networks with Swift Tensorflow, pp. 99–107, 2019.Google Scholar
- [31] J. Tagliabue , SIGIR 2021 E-Commerce Workshop Data Challenge, vol. 1, no. 1. Association for Computing Machinery, 2021.Google Scholar
- [32] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-December, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.Google ScholarCross Ref
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