Work

Data augmentation using Fast and Adaptive Bidimensional Empirical Mode Decomposition for pulmonary tuberculosis diagnosis in chest X-rays

Year: 2025

Type: article

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.

Source: The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences

DOI: 10.5194/isprs-archives-xlviii-2-w9-2025-249-2025

Authors: Yuliya D. Senotova, Yakov A. Pchelintsev, Andrey S. Krylov

Institution: Lomonosov Moscow State University

Language: English

Topic: COVID-19 diagnosis using AI

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4413977217