COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

By Muhammad Shoaib Farooq, Abdul Hafeez. arXiv.org, 2020

493 citations recorded by the source corpus.

In the last few months, the novel COVID19 pandemic has spread all over the world. Due to its easy transmission, developing techniques to accurately and easily identify the presence of COVID19 and distinguish it from other forms of flu and pneumonia is crucial. Recent research has shown that the chest Xrays of patients suffering from COVID19 depicts certain abnormalities in the radiography. However, those approaches are closed source and not made available to the research community for re-producibility and gaining deeper insight. The goal of this work is to build open source and open access datasets and present an accurate Convolutional Neural Network framework for differentiating COVID19 cases from other pneumonia cases. Our work utilizes state of the art training techniques including progressive resizing, cyclical learning rate finding and discriminative learning rates to training fast and accurate residual neural networks. Using these techniques, we showed the state of the art results on the open-access COVID-19 dataset. This work presents a 3-step technique to fine-tune a pre-trained ResNet-50 architecture to improve model performance and reduce training time. We call it COVIDResNet. This is achieved through progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3 pixels and fine-tuning the network at each stage. This approach along with the automatic learning rate selection enabled us to achieve the state of the art accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals. This model can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/049ea432acc08b04a3e21c390f62be3d845a2e2c

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, background): “…network 95.70 Chowdhury, Rahman, Khandakar, Mazhar, Kadir, Mahbub, Islam, Khan, Iqbal and Al-Emadi [30] Transfer Learning 97.94 Farooq and Hafeez [65] Transfer Learning 96.20 Ucar and Korkmaz [41] Bayes-SqueezeNet 98.30 Apostolopoulos and Mpesiana [66] Transfer Learning 93.48 Xu, Jiang, Ma, Du,…”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “Since the time series used in this study are also nonlinear and irregular, so it can be said that the results of this study are consistent with previous studies.”
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, methodology): “Based on ResNet-50 [27], COVID-ResNet [28] is proposed to differentiate three types of COVID-19 infections from normal pneumonia individuals.”
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, unknown): “Farooq and Hafeez [18] present a ResNet based framework in a four-class dataset with augmentation.”
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases (2021, unknown): “[39] ResNet-50 (COVID-ResNet) Chest x- ray 96.”
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach (2020, background): “Farooq and Hafeez [24] proposed a fine-tuned and pre-trained ResNet-50 architecture, COVID-ResNet, for COVID-19 pneumonia screening.”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “[140] provide the steps to fine-tune a pre-trained ResNet-50 [139] architecture to improve model performance for detecting COVID-19 related abnormalities (called COVID-ResNet).”
Using X-ray images and deep learning for automated detection of coronavirus disease (2020, background): “In order to identify Covid19 cases from other Pneumonia (Bacteria and virus) and normal cases, Farooq and Hafeez (2020) proposed a convolutional neural network (CNN) framework.”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, unknown): “Farooq and Hafeez [16] presented a multi-stage fine-tuning scheme for pre-trained ResNet-50 architecture.”