The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

Abstract

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χMcNemar′s statistic2=163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

“…Data augmentation can improve model generalization by increasing the variations in the training set [ 73 ]. Image processing techniques, including standardization, normalization, reorientation, registration, and histogram matching, can be used to harmonize images sourced from different origins and remove domain bias.…”

Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem

“…But many authors utilise small private dataset together with larger public datasets. An exemplary case is the use of private datasets as external test data, therewith assessing the transportability of models trained on (merged) public datasets to the private test data population and thus to the underlying hospital setting ( Park, Kim, Oh, Seo, Lee, Kim, Moon, Lim, Ye , Kim, Park, Oh, Seo, Lee, Kim, Moon, Lim, Ye , Elgendi, Nasir, Tang, Smith, Grenier, Batte, Spieler, Leslie, Menon, Fletcher, et al., 2021 , Robinson, Trivedi, Blazes, Ortiz, Desbiens, Gupta, Dodhia, Bhatraju, Liles, Lee, et al., 2021 ).…”

Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes

“…This could prove useful for classification problems where there is a well-defined loss/cost for misclassified samples. (Elgendi et al, 2021) proposes a solution to the second challenge by introducing and comparing four data augmentation methods for artificially increasing the number of training samples of X-ray images, while performing Covid-19 pneumonia detection using a CNN. The methods uses combinations of random rotations, shear, translation, horizontal-and vertical flipping among other data augmentation methods.…”