Open Access
ARTICLE
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:
Journal on Internet of Things 2021, 3(2), 39-51. https://doi.org/10.32604/jiot.2021.014877
Received 07 January 2021; Accepted 11 April 2021; Issue published 15 July 2021
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.
Keywords
Cite This Article
S. Sarker, L. Tan, W. Ma, S. Rong, O. B. Kwapong
et al., "Multi-classification network for identifying covid-19 cases using deep convolutional neural networks,"
Journal on Internet of Things, vol. 3, no.2, pp. 39–51, 2021.
Citations