Data augmentation using Fast and Adaptive Bidimensional Empirical Mode Decomposition for pulmonary tuberculosis diagnosis in chest X-rays
Keywords: chest X-ray, medical images, pulmonary tuberculosis diagnosis, data augmentation, deep learning, Fast and Adaptive Bidimensional Empirical Mode Decomposition
Abstract. Automation in medical diagnostics based on machine learning algorithms relies heavily on the quality and volume of training data. For pulmonary tuberculosis diagnosis in chest X-rays image quality varies due to differences in equipment and acquisition conditions, and the availability of high-quality data is limited due to legal constraints and the smaller size of public datasets compared to those for some other lung diseases. Additionally, concerns regarding cross-dataset compatibility and discrepancies between training and target data distributions further complicate the analysis. To mitigate these issues, we propose a data augmentation technique utilizing the Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) algorithm. Experiments have demonstrated its effectiveness for pulmonary tuberculosis diagnosis in chest X-rays.