Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

By A. Ardakani, Alireza Rajabzadeh Kanafi, U. R. Acharya, Nazanin Khadem, A. Mohammadi, A. Rajabzadeh, Usha R. Acharya. 2020

734 citations recorded by the source corpus.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “Related work A number of studies have proposed deep learning systems based on chest CT imaging to distinguish COVID-19 cases from non-COVID-19 cases (which may include both normal and abnormal cases) [16, 20, 21, 22, 23, 24, 25, 26, 27, 17, 28, 29, 30]. Many of the proposed systems further identify ”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “Then, given the speciﬁed states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, background): “Ardakani, Kanafi, Acharya, Kha-dem and Mohammadi [26] presented the state-of-the-art CNN architectures mentioned above to differentiate COVID-19 cases from other (non-COVID) cases.”
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review (2020, neutral): “[28] Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A.”
Real-world data: a brief review of the methods, applications, challenges and opportunities (2022, methodology): “For example, ML is widely applied in in health informatics to generate RWE and formulate personalized healthcare [86–90] and was successfully employed on RWD collected during the COVID-19 pandemic to help understand the disease and evaluate its prevention and treatment strategies [91–95].”
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, methodology): “(2020) used a small dataset of 108 COVID-19 and 86 non-COVID-19 patients to evaluate ten different DL models and achieved a 99% overall accuracy [92].”
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays (2020, neutral): “Ardakani et al. [47] propose a deep learning based approach for distinguishing from COVID-19 from non-COVID-19.”
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases (2021, neutral): “From Table 1, it seems that our result is not better than [27,28].”