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Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images by Mohammad Momeny; Ali Asghar Neshat; Mohammad Arafat Hussain; Solmaz Kia; Mahmoud Marhamati; Ahmad Jahanbakhshi; Ghassan Hamarneh is a scholarly article available to read on EtoBox.

What is Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images about?

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). Th

Author

Mohammad Momeny; Ali Asghar Neshat; Mohammad Arafat Hussain; Solmaz Kia; Mahmoud Marhamati; Ahmad Jahanbakhshi; Ghassan Hamarneh

Publisher

Elsevier BV

Published

2021

Language

EN