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Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

Computers, Materials & Continua 2021年第5期67卷 2093-2110页

作者： Mohamad Mahmoud al Rahhal Yakoub Bazi Rami M.Jomaa Mansour Zuair naif al ajlan Department of Applied Computer Science College of Applied Computer EngineeringKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11362Saudi Arabia

With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is *** routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening *** the good choices for an accurate and fast test to screen COVID-19 are deep learning *** this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is *** EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan *** addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan *** proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is *** proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and *** proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.

关键词： COVID-19 deep learning computed tomography multi-scale features atrous convolution adversarial examples

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