Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks

The new type of coronavirus disease (COVID-19), which started in Wuhan, China in December 2019, continues to spread rapidly affecting the whole world. It is essential to have a highly sensitive diagnostic screening tool to detect the disease as early as possible. Currently, chest CT imaging is prefe...

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Bibliographic Details
Published in:arXiv.org 2020-12
Main Authors: Öksüz, Coşku, Urhan, Oğuzhan, Güllü, Mehmet Kemal
Format: Article
Language:English
Subjects:
Chest
Computed tomography
Coronaviruses
COVID-19
Deep learning
Diagnostic software
Diagnostic systems
Evaluation
Feature extraction
Feature maps
Health and safety screening
Health care facilities
Lightweight
Machine learning
Medical imaging
Model accuracy
Parameter sensitivity
Pneumonia
Radiation dosage
Radiography
Viral diseases
X ray imagery
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Summary:The new type of coronavirus disease (COVID-19), which started in Wuhan, China in December 2019, continues to spread rapidly affecting the whole world. It is essential to have a highly sensitive diagnostic screening tool to detect the disease as early as possible. Currently, chest CT imaging is preferred as the primary screening tool for evaluating the COVID-19 pneumonia by radiological imaging. However, CT imaging requires larger radiation doses, longer exposure time, higher cost, and may suffer from patient movements. X-Ray imaging is a fast, cheap, more patient-friendly and available in almost every healthcare facility. Therefore, we have focused on X-Ray images and developed an end-to-end deep learning model, i.e. Ensemble-CVDNet, to distinguish COVID-19 pneumonia from non-COVID pneumonia and healthy cases in this work. The proposed model is based on a combination of three lightweight pre-trained models SqueezeNet, ShuffleNet, and EfficientNet-B0 at different depths, and combines feature maps in different abstraction levels. In the proposed end to-end model, networks are used as feature extractors in parallel after fine-tuning, and some additional layers are used at the top of them. The proposed model is evaluated in the COVID-19 Radiography Database, a public data set consisting of 219 COVID-19, 1341 Healthy, and 1345 Viral Pneumonia chest X-Ray images. Experimental results show that our lightweight Ensemble-CVDNet model provides 98.30% accuracy, 97.78% sensitivity, and 97.61% F1 score using only 5.62M parameters. Moreover, it takes about 10ms to process and predict an X-Ray image using the proposed method using a mid level GPU. We believe that the method proposed in this study can be a helpful diagnostic screening tool for radiologists in the early diagnosis of the disease.
ISSN:2331-8422