A Deep learning based data augmentation method to improve COVID-19 detection from medical imaging
Djamila Romaissa Beddiar, Mourad Oussalah, Usman Muhammad, Tapio Seppänen
Abstract
The worldwide spread of the Coronavirus pandemic and its huge impact challenged medical and research communities to explore novel approaches for medical diagnosis from medical imaging. However, the availability of training samples makes it difficult to implement efficient deep learning AI based solutions. In this regard, we propose new data augmentation strategies to compensate for this limitation. Our approach uses noise estimation to preserve the noise/signal ratio of the original images, while performing data augmentation. A pre-trained image Denoising Deep Neural Network DnCNN is used to calculate various sets of augmented images. First, original images are denoised. A Gaussian noise is then applied on the original images with the estimated variance computed for each class to create noisy images which are denoised again with the same DnCNN. Created subsets are fused with the original one and introduced to a Temporal Convolutional Network TCN for classification into COVID-19 and no-COVID-19 classes. We evaluated the performance of our proposal using some pre-trained networks and a convolutional neural network on three popular Covid-19 imaging datasets and the results were compared to several state-of-the-art models, demonstrating the feasibility and technical soundness of our proposal. The outcomes are also investigated to provide some explainability cues.