Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems

Author(s): Saleh Albahli*, Waleed Albattah

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 17 , Issue 8 , 2021

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Graphical Abstract:


Objective: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.

Methods: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.

Results: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.

Conclusion: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

Keywords: Deep transfer learning, coronavirus, X-ray, CNN, inceptioNetV3, inceptionResNetV2.

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Article Details

Year: 2021
Published on: 23 November, 2020
Page: [973 - 980]
Pages: 8
DOI: 10.2174/1573405616666201123120417

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