COVID -19 Predictions using Transfer Learning based Deep Learning Model with Medical Internet of Things
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Abstract
Early detection of COVID-19 may help medical expert for proper treatment plan and infection control. Internet of Things (IoT) has vital improvement in many areas including medical field. Deep learning also provide tremendous improvement in the field of medical. We have proposed a Transfer learning based deep learning model with medical Internet of Things for predicting COVID-19 from X-ray images. In the proposed method, the X ray images of patient are stored in cloud storage using internet for wide access. Then, the images are retrieved from cloud and Transfer learning based deep learning models namely VGG-16, Inception, Alexnet, Googlenet and Convolution neural Network models are applied on the X-rays images for predicting COVID 19, Normal and pneumonia classes. The pre-trained models helps to the effectiveness of deep learning accuracy and reduced the training time. The experimental analysis show that VGG -16 model gives accuracy of 99% for detecting COVID19 than other models.
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C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, (2018) A survey on deep transfer learning. In International conference on artificial neural networks. Springer, 270–279.
Narin, C. Kaya, and Z. Pamuk, (2021) Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24: 1–14.
CDC COVID-19 Response Team. Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019— United States, February 12–March 28, 2020. Morb. Mortal. Wkly. Rep. 2020, 69, 382–386
Moriyama, M., Hugentobler, W. J., & Iwasaki, A. (2020) Seasonality of respiratory viral infections. Annual review of virology, 7: 1-20.
O. B. Akan, S. Andreev, and C. Dobre, (2019) Internet of things and sensor Networks. IEEE Communications Magazine, 57: 34-34.
P. Partila, J. Tovarek, G. H. Ilk, J. Rozhon, and M. Voznak (2020) Deep learning serves voice cloning: how vulnerable are automatic speaker verification systems to spoofing trials? IEEE Communications Magazine, 58: 100–105
Iskanderani, A. I., Mehedi, I. M., Aljohani, A. J., Shorfuzzaman, M., Akther, F., Palaniswamy, T., ... & Alam, A. (2021). Artificial intelligence and medical internet of things framework for diagnosis of coronavirus suspected cases. Journal of Healthcare Engineering, 2021:1-7
Abir, S. M., Islam, S. N., Anwar, A., Mahmood, A. N., & Oo, A. M. T. (2020) Building resilience against COVID-19 pandemic using artificial intelligence, machine learning, and IoT: A survey of recent progress
R. P. Singh, M. Javaid, A. Haleem, R. Vaishya, and S. Al (2020) Internet of Medical Things (IoMT) for orthopaedic in COVID-19 pandemic: Roles, challenges, and application. J. Clin. Orthopaedics Trauma, 11:713–717
T. Yang, M. Gentile, C.-F. Shen, and C.-M. Cheng (2020) Combining pointof-care diagnostics and Internet of Medical Things (IoMT) to combat the COVID-19 pandemic. Diagnostics (Basel), 10:1-3.
M. T. Vafea et al. Emerging technologies for use in the study, diagnosis, and treatment of patients with COVID-19 (2020), Cell. Mol. Bioeng., 13:249–257
F. Shi et al., (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID19, IEEE Rev. Biomed. Eng., early access, .14: 4-15.
L. Wynants et al., (2020) Prediction models for diagnosis and prognosis of COVID-19 infection: Systematic review and critical appraisal, Brit. Med. J., 369: 1-16
Narin, A.; Kaya, C.; Pamuk, Z (2020) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern analysis and applications, 24: 1207-20
Lu, J.; Gu, J.; Li, K.; Xu, C.; Su, W.; Lai, Z.; Zhou, D.; Yu, C.; Xu, B.; Yang, Z. (2020) COVID-19 outbreak associated with air conditioning in restaurant, Guangzhou, China, 2020. Emerg. Infect.
Sajadi, M.M.; Habibzadeh, P.; Vintzileos, A.; Shokouhi, S.; Miralles-Wilhelm, F.; Amoroso, A. (2021) Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19, 1-16.
Van Doremalen N, Bushmaker T, Morris DH, Holbrook MG, Gamble A, Williamson BN, Tamin A, Harcourt JL, Thornburg NJ, Gerber SI, Lloyd-Smith JO (2020) Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1 New England journal of medicine https:// doi: 10.1056/NEJMc2004973
Loey M, Manogaran G, Taha MH, Khalifa NE (2021) A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement doi: 10.1016/j.measurement.2020.108288.
Hemdan EE, Shouman MA, Karar ME. Covidx-net (2003) A framework of deep learning classifiers to diagnose covid-19 in x-ray images. https://doi.org/10.48550/arXiv.2003.11055.
Wang L, Lin ZQ, Wong A (2020) Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports https://doi.org/10.1038/s41598-020-76550-z.
Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks Physical and engineering sciences in medicine. https://doi: 10.1007/s13246-020-00865-4.
Sadoughi F, Behmanesh A, Sayfouri N (2020) Internet of things in medicine: a systematic mapping study. Journal of Biomedical Informatics. https://doi.org/10.1016/j.jbi.2020.103383
Karmakar A, Dey N, Baral T, Chowdhury M, Rehan M.M (2019) Industrial Internet of Things: A Review. 2019 International Conference on Opto-Electronics and Applied Optics (Optronix),
Wu Q, Ding G, Xu Y, Feng S, Du Z, Wang J, Long K (2014) Cognitive internet of things: a new paradigm beyond connection. IEEE Internet of Things journal .
Kaur T, Gandhi TK. Automated brain image classification based on VGG-16 and transfer learning. (2019) International Conference on Information Technology (ICIT). httpS://doi:10.1109/ICIT48102.2019.00023
Reddy ASB, Juliet DS (2019) Transfer learning with ResNet-50 for malaria cell-image classification. In Proceedings of the IEEE 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, htpps://doi:10.1109/ICCSP.2019.8697909
Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW.(2020) Efficient pneumonia detection in chest xray images using deep transfer learning. Diagnostics. https://doi: 10.3390/diagnostics10060417
Melchiorre MG, Chiatti C, Lamura G, Torres-Gonzales F, Stankunas M, Lindert J, Ioannidi-Kapolou E, Barros H, Macassa G, Soares JF (2013)Social support, socio-economic status, health and abuse among older people in seven European countries. PloS one. https://doi.org/10.1371/journal.pone.0242301
More S, Singla J, Verma S, Ghosh U, Rodrigues JJ, Hosen AS, Ra IH(2020) Security assured CNN-based model for reconstruction of medical images on the internet of healthcare things. IEEE Access. https://doi: 10.1109/ACCESS.2020.3006346
Alnumay W, Ghosh U, Chatterjee P(2019) A Trust-Based predictive model for mobile ad hoc network in internet of things. Sensors. https://doi.org/10.3390/s19061467
Nagarajan SM, Deverajan GG, Chatterjee P, Alnumay W, Ghosh U(2021) Effective task scheduling algorithm with deep learning for internet of health things (ioht) in sustainable smart cities. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2021.102945
Houssein EH, Ahmad M, Hosney ME, Mazzara M(2021) Classification Approach for COVID-19 Gene Based on Harris Hawks Optimization. InArtificial Intelligence for COVID-19 DOI: https://doi.10.1007/978-3-030-69744-0_32
Sergey M, Manuel M, Giancarlo S, Aldo S, Antonio V, Aldo S, Antonio V (2020) Covid 19- How Really is the Epidemiological Curve? Epidemiological Curve Growth Rate is Less than One Biomedical Journal of Scientific & Technical Research. https:doi: 10.26717/BJSTR.2020.27.004557 .
Kaggle. COVID-19 Radiography Dataset. Available online: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/activity (accessed on 25 May 2021).