Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT s
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ORIGINAL ARTICLE
Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans Parisa gifani1 · Ahmad Shalbaf2
· Majid Vafaeezadeh3
Received: 23 May 2020 / Accepted: 23 October 2020 © CARS 2020
Abstract Purpose COVID-19 has infected millions of people worldwide. One of the most important hurdles in controlling the spread of this disease is the inefficiency and lack of medical tests. Computed tomography (CT) scans are promising in providing accurate and fast detection of COVID-19. However, determining COVID-19 requires highly trained radiologists and suffers from inter-observer variability. To remedy these limitations, this paper introduces an automatic methodology based on an ensemble of deep transfer learning for the detection of COVID-19. Methods A total of 15 pre-trained convolutional neural networks (CNNs) architectures: EfficientNets(B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50 and Inception_resnet_v2 are used and then fine-tuned on the target task. After that, we built an ensemble method based on majority voting of the best combination of deep transfer learning outputs to further improve the recognition performance. We have used a publicly available dataset of CT scans, which consists of 349 CT scans labeled as being positive for COVID-19 and 397 negative COVID-19 CT scans that are normal or contain other types of lung diseases. Results The experimental results indicate that the majority voting of 5 deep transfer learning architecture with EfficientNetB0, EfficientNetB3, EfficientNetB5, Inception_resnet_v2, and Xception has the higher results than the individual transfer learning structure and among the other models based on precision (0.857), recall (0.854) and accuracy (0.85) metrics in diagnosing COVID-19 from CT scans. Conclusion Our study based on an ensemble deep transfer learning system with different pre-trained CNNs architectures can work well on a publicly available dataset of CT images for the diagnosis of COVID-19 based on CT scans. Keywords COVID-19 · CT · Transfer learning · Convolutional neural network · Ensemble model
Introduction COVID-19 is an infectious disease that has infected more than 4.5 million individuals all over the world until May 14 in 2020 [1]. The current tests for diagnosis of this disease are mostly based on reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR test kits are in
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Ahmad Shalbaf [email protected]
1
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
huge shortage and take 4–6 h to obtain results, which are a long time compared with the rapid spreading rate of COVID19. As a result, many infected cases cannot be detected prompt
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