I. J. of Electrical & Electronics Research
Research Article | 
Detection and Severity Identification of Covid-19 in Chest X-ray Images Using Deep Learning
Author(s) : Vadthe Narasimha1 and Dr. M. Dhanalakshmi 2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2, Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 30 June 2022
e-ISSN : 2347-470X
Page(s) : 364-369
Abstract
COVID-19 pandemic is causing a significant flare-up, seriously affecting the wellbeing and life of many individuals all around the world. One of the significant stages in battling COVID-19 is the capacity to recognize the tainted patients early and put them under exceptional consideration. In the proposed model we used deep learning-based exception Net under transfer learning paradigm. We trained the proposed model using chest-X rays collected from the open-source dataset (COVID -19 Dataset) using K10 cross-validation. We further calculated the severity in the covid classified images by the model using radiologist ground truth. We achieved an accuracy of 96.1% in the classification, and we are able to calculate the severity of the COVID -19 within the range of 75-100 % risk. Our proposed model successfully classified the COVID chest x-rays with severity measure
Keywords: COVID-19, Machine Learning, Xecption, CNN, RT-PCR, VGG19, ResNet and Inception
Vadthe Narasimha, JNTUH Research Scholar, Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India; Email: chinna.narasimha63@gmail.com
Dr. M. Dhanalakshmi , Department of Information Technology, JNTUH, Hyderabad, India
[1] Sarbjit Singh, Kulwinder Singh Parmar et al.Study of arima and least square support vector machine (ls-svm) models for the prediction of sars-cov-2 confirmed casesin the most affected countries. Chaos, Solitons & Fractals,139:110086, 2020.[Cross Ref]
[2] Hamed Tabrizchi, Amir Mosavi,et al. Rapid covid-19 diagnosis using deep learn-ing of the computerized tomography scans. In 2020 IEEE 3rd International Conference and Workshop in Óbuda on Electri-cal and Power Engineering (CANDO-EPE), pages 000173–000178. IEEE, 2020.[Cross Ref]
[3] Hongmei Yue, Qian Yu, Chuan Liu, et al. Machine learning-based ct radiomics method for predicting hospital stay in patients with pneumonia associated with sars-cov-2 infection:a multicenter study. Annals of translational medicine, 8(14),2020.[Cross Ref]
[4] Weiya Shi, Xueqing Peng, Tiefu Liu, et al. A deep learning-based quantitative computed tomography model in predicting the severity of covid-19: A ret-rospective study in 196 patients. Annals of Translational Medi-cine, 9(3), 2021.[Cross Ref]
[5] Li Yan, Hai-Tao Zhang, Jorge Goncalves, et al. An interpretable mortality prediction model for covid-19 patients. Nature Machine Intelligence, pages 1–6,2020.[Cross Ref]
[6] Aya Salama, Ashraf Darwsih, et al.. Artifi-cial intelligence approach to predict the covid-19 patient’s recov-ery. In Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, pages 121–133. Springer, 2021.[Cross Ref]
[7] Rajan Gupta, Gaurav Pandey et al.. Seir and regression model based covid-19 out-break predictions in india. medRxiv, 2020.[Cross Ref]
[8] Xingdong Chen and Zhenqiu Liu. Early prediction of mortal-ity risk among severe covid-19 patients using machine learning.medRxiv, 2020.[Cross Ref]
[9] Matheus Henrique et al.Short-term forecasting covid-19 cumulative confirmed cases: Perspec-tives for brazil. Chaos, Solitons & Fractals, page 109853, 2020.[Cross Ref]
[10] Milind Yadav, Murukessan Perumal, and M Srinivas. Analysis on novel coronavirus (covid-19) using machine learning methods.Chaos, Solitons & Fractals, 139:110050, 2020.[Cross Ref]
[11] João Matos, Francesco Paparo, et al. Evaluation of novel coronavirus disease (covid-19) using quantitative lung ct and clinical data: prediction of short-term outcome. European radiology experimental, 4(1):1–10, 2020.[Cross Ref]
[12] Saqib M. Forecasting covid-19 outbreak progression using hybrid polynomial-bayesian ridge regression model. Appl Intell. 2021;51(5):2703–13.[Cross Ref]
[13] Feng Shi, Liming Xia, et al.. Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:2003.09860, 2020.[Cross Ref]
[14] Celestine Iwendi, et al.. Covid-19 patient health prediction using boosted random forest algorithm. Frontiers in public health, 8:357, 2020.[Cross Ref]
[15] Brinati D, Campagner et al.. Detection of covid-19 infection from routine blood exams with machine learning: a feasibility study. J Med Syst. 2020;44(8):1–12.[Cross Ref]
[16] LJ Muhammad, Ebrahem et al.. Supervised machine learning models for predic-tion of covid-19 infection using epidemiology dataset. SN computer science, 2(1):1–13, 2021.[Cross Ref]
[17] Ali Mohammad Alqudah et al.. Covid-2019 detection using x-ray images and artificial intelligence hybrid systems. https://doi. org/10.13140/RG, 2(16077.59362):1, 2020. [Cross Ref]
[18] Seung Hoon Yoo, et al. Deep learning-based decision-tree classifier for covid-19 diagnosis from chest x-ray imaging. Frontiers in medicine, 7:427, 2020.[Cross Ref]
[19] Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, et al. A deep learning algorithm using ct images to screen for corona virus disease (covid-19). Eur Radiol. 2021;31(8):6096–104.[Cross Ref]
[20] Bressem, K.K., Adams, L.C., Erxleben, C. et al. Comparing different deep learning architectures for classification of chest radiographs. Sci Rep 10, 13590 (2020). https://doi.org/10.1038/s41598-020-70479-z.[Cross Ref]
Vadthe Narasimha and Dr. M. Dhanalakshmi (2022), Detection and Severity Identification of Covid-19 in Chest X-ray Images Using Deep Learning. IJEER 10(2), 364-369. DOI: 10.37391/IJEER.100250.