An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis

Abstract

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries and also there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this paper proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs) including VGG16, ResNet50, DenseNet121, and InceptionResNetV2 are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modelling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image datasets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and AUC. Also it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.

arXiv comment9 pages, 9 figures, 3 tables

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Cited by in corpus (6)

• Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
• UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
• Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays Image
• Hamming Distance Tolerant Content-Addressable Memory (HD-CAM) for Approximate Matching Applications
• Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images
• Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes