Abstract
Coronaviruses are mainly a big family of viruses that are highly capable of causing illness both in animals and humans. The scientific name of the most recently discovered corona virus disease is COVID-19. Most of the countries are performing the manual testing which is beneficial to know the actual situation, feature of the disease, so that appropriate decision can be taken. The main drawbacks of manual testing is that it is very expensive, sparse availability of testing kits, inefficient blood test, and minimum 5–6 h will require to generate the report of blood test. So in these circumstances, deep learning plays a crucial role to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and provide fast and efficient treatment to the effected patients. The developed model consists of three groups: COVID-19, Influenza-A viral pneumonia, and healthy cases. Our proposed detection model got 98.78% accuracy. In this study, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods from CT scan images. We have developed two models (a) Inception residual recurrent convolutional neural network with transfer learning (TL) approach for COVID-19 detection and (b) NABLA-N network model for segmenting the regions infected by COVID-19.
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References
Wang C, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. Lancet. https://doi.org/10.1016/S0140-6736(20)30185-9
Coronavirus Outbreak. Available at: https://www.worldometers.info/coronavirus/. Accessed 23 Feb 2020
WHO. int Europe. Middle East respiratory syndrome coronavirus (MERS-CoV). Available from: https://www.who.int/emergencies/mers-cov/en/, April 2020
Chan-Yeung M, Xu RH (2003) SARS Epidemiol Respirol 8:S9–S14
Guan Y, Zheng B, He Y, Liu X, Zhuang Z, Cheung C, Luo S, Li P, Zhang L, Guan Y (2003) Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302:276–278
Decaro N, Mari V, Elia G, Addie DD, Camero M, Lucente MS, Martella V, Buonavoglia C (2010) Recombinant canine coronaviruses in dogs, Europe Emerg Infect Dis 16:41–47. [CrossRef] [PubMed]
mers-cov/mers-outbreaks.html (accessed on 11 March 2020)
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 395:497–506 [CrossRef]
Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, Si H-R, Zhu Y, Li B, Huang C-L (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579:270–273 [CrossRef]
Rothe C, Schunk M, Sothmann P et al (2020) Transmission of 2019- nCoV infection from an asymptomatic contact in Germany. N Engl J Med. https://doi.org/10.1056/NEJMc2001468
Kampf G, Todt D, Pfaender S, Steinmann E (2020) Persistence of coronaviruses on inanimate surfaces and its inactivation with biocidal agents. J Hosp Infect. pii: S0195–6701(20)30046–3
World Health Organization. Situation reports. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/. Accessed 22 Feb 2020
Cheng ZJ, Shan J (2019) novel coronavirus: where we are and what we know. Infection 2020:1–9. https://doi.org/10.1007/s15010-020-01401-y
Chen N, Zhou M, Dong X et al (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395:507–513
J.P.C.a.P.M.a.L.Dao (2020) COVID-19 image data collection. Available: https://github.com/ieee8023/covid-chestxray-dataset
I. S. o. M. a. I (2020) Radiology. COVID-19 DATABASE. Available: https://www.sirm.org/category/senza-categoria/covid-19/
Rahman T (2020) COVID-19Radiography database. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Improved inception-residual convolutional neural network for object recognition. Neural Comput Appl 1–15
Alom MZ, Aspiras T, Taha TM, Asari VK (2020) Skin cancer segmentation and classification with NABLA-N and inception recurrent residual convolutional networks. Published in SPIE Medical Imaging Conference, 15–20 February 2020, Houston, Texas, USA
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers R (2017) ChestX-ray14: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Wang L, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images
Kaur B, Sharma M, Mittal M, Verma A, Goyal LM, Hemanth DJ (2018) An improved salient object detection algorithm combining background and foreground connectivity for brain image analysis. Comput Electr Eng 71:692–703
Mittal A, Kumar D, Mittal M, Saba T, Abunadi I, Rehman A, Roy S (2020) Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images. Sensors 20(4):1068
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Ray, A., Tiwari, A., Chandra Mouli, D. (2021). Early Screening of COVID-19 from Chest CT Using Deep Learning Technique. In: Roy, S., Goyal, L.M., Mittal, M. (eds) Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-16-0538-3_11
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DOI: https://doi.org/10.1007/978-981-16-0538-3_11
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