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Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks

Sajib Sarker, Ling Tan*, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh

School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Ling Tan. Email: email

Journal on Internet of Things 2021, 3(2), 39-51. https://doi.org/10.32604/jiot.2021.014877

Abstract

The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 4-class (Normal vs. COVID-19 vs. Viral Pneumonia vs. Bacterial Pneumonia) demonstrated that the pre-trained model DenseNet201 provides the highest classification performance (accuracy: 92.54%, precision: 93.05%, recall: 92.81%, F1-score: 92.83%, specificity: 97.47%). Notably, the DenseNet201 (4-class classification) pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven models. Important to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become available. The proposed multi-classification network (COV-MCNet) significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic.

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APA Style
Sarker, S., Tan, L., Ma, W., Rong, S., Kwapong, O.B. et al. (2021). Multi-classification network for identifying COVID-19 cases using deep convolutional neural networks. Journal on Internet of Things, 3(2), 39-51. https://doi.org/10.32604/jiot.2021.014877
Vancouver Style
Sarker S, Tan L, Ma W, Rong S, Kwapong OB, Darteh OF. Multi-classification network for identifying COVID-19 cases using deep convolutional neural networks. J Internet Things . 2021;3(2):39-51 https://doi.org/10.32604/jiot.2021.014877
IEEE Style
S. Sarker, L. Tan, W. Ma, S. Rong, O.B. Kwapong, and O.F. Darteh "Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks," J. Internet Things , vol. 3, no. 2, pp. 39-51. 2021. https://doi.org/10.32604/jiot.2021.014877

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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