An approach for recognizing COVID-19 cases using convolutional neural networks applied to CT scan images

Cuong Do; Lan Vu

doi:10.1117/12.2576276

21 August 2020 An approach for recognizing COVID-19 cases using convolutional neural networks applied to CT scan images

Cuong Do, Lan Vu

Proceedings Volume 11510, Applications of Digital Image Processing XLIII; 1151034 (2020) https://doi.org/10.1117/12.2576276
Event: SPIE Optical Engineering + Applications, 2020, Online Only

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Abstract

This study aims to investigate an automated approach using Convolutional Neural Network (CNN) to efficiently classify COVID-19 cases vs healthy cases using chest CT images. Convolutional Neural Network (CNN) is a class of deep neural networks, usually applied to analyzing image data, and can learn features effectively from images in comparison to the traditional method with image segmentation, feature extraction/selection and classification steps. Several models using pre-trained weights, including VGG16, VGG19, InceptionV3, InceptionResNetV2, Xception, DenseNet121, DenseNet169, and DenseNet201 were investigated. Overfitting was handled by randomly dropping nodes during training, augmenting training data, as well as using the validation set. We concluded that a CNN approach can detect COVID-19 using CT features, and DenseNet201is the highest performing model.

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Cuong Do and Lan Vu "An approach for recognizing COVID-19 cases using convolutional neural networks applied to CT scan images", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 1151034 (21 August 2020); https://doi.org/10.1117/12.2576276

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Cited by 5 scholarly publications.

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KEYWORDS

Computed tomography

Convolutional neural networks

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