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

Abstract

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). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy .

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images

“…Adding some noise to data (e.g., the impulse noise, the Gaussian noise) is a strategy for data augmentation [8] . We have employed a learning-to-augment strategy [8] using noisy X-ray images to generate the new data.…”

“…A CNN is an effective tool for image classification, which has been used in various fields such as health, economics, and agriculture [4] , [5] , [6] , [7] , [8] , [9] , [10] . Last year, various types of CNNs were extensively used in COVID-19 detection in medical images.…”

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

“…The ideal weight matrices for the neurons are determined using an optimization method that minimizes the loss function while taking into account the input data points. The loss function decreases and tends to move towards zero once a set number of epochs are completed for training the neural network [ 23 ]. CNN has indeed been employed in practically every aspect of healthcare, and it is one of the most enticing methodologies for researchers.…”

“…When there is a chance that image data will be contaminated by noise, noise-based data augmentation in DL is usually used. The approach of data augmentation by noise addition increases the robustness and generalization of CNNs [ 23 ]. So, a substantial amount of training data is necessary to train a CNN method, which may be regarded as a key constraint in DL techniques [ 24 ].…”

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

“…These variants present a challenge in dealing with the COVID-19 pandemic [ 1 ]. Governments have begun to fund COVID-19 vaccine development and research [ 2 ]. However, the world does not know how long a vaccine would stay effective as the virus mutates.…”