Frequency Filtering For Data Augmentation In X-Ray Image Classification

A sufficiently large dataset is needed to solve tasks based on deep learning. However, using medical datasets for deep learning is challenging because their access is limited. In this paper, we propose a novel data augmentation paradigm that can be used in image classification of convolutional neural network-based approaches. As the frequency domain has more unique patterns than the spatial domain, more diverse patterns can be obtained by arbitrarily changing the frequency domain patterns of the given image. First, we assume that the meaningful patterns in the frequency domain are typically distributed in high-intensity regions in the Fourier spectrum. Next, we select the angle to reject meaningful patterns from the Fourier spectrum. Subsequently, we generate a novel mask pattern and remove specific frequency patterns in a fast Fourier transform image. Thereafter, we apply an inverse fast Fourier transform to the rejected frequency image to convert it back to the spatial domain. Our method achieves consistent performance improvements on X-ray images using various backbones. On average, we achieved 3.30%, 7.09%, 7.75%, 8.14%, and 4.91% improvements on the accuracy, precision, recall, F1-score, and AUC over the previous methods.