@Article{jiot.2021.014877,
AUTHOR = {Sajib Sarker, Ling Tan, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh},
TITLE = {Multi-Classification Network for Identifying COVID-19 Cases Using Deep  Convolutional Neural Networks},
JOURNAL = {Journal on Internet of Things},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {39--51},
URL = {http://www.techscience.com/jiot/v3n2/43100},
ISSN = {2579-0080},
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.},
DOI = {10.32604/jiot.2021.014877}
}