2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-2.5
Paper Title FREQUENCY FILTERING FOR DATA AUGMENTATION IN X-RAY IMAGE CLASSIFICATION
Authors Ju-Hyeon Nam, Sang-Chul Lee, Inha University, Republic of Korea
SessionBIO-2: Biomedical Signal Processing 2
LocationArea D
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.