Tuberculosis Detection Comparison by Using Preprocessed and Non-preprocessed Chest X-Ray Images Contribución en una conferencia

Nombre del libro

47th Mexican Conference on Biomedical Engineering - Proceedings of CNIB 2024 - Signal Processing And Bioinformatics Congreso Nacional de Ingeniería Biomédica CNIB Hermosillo

editorial

Springer Science and Business Media Deutschland GmbH

Autor

Axel Yahir Ramírez-Ángel
Rubén Saúl Jiménez-Fernández

Resumen

Tuberculosis (TB) remains a significant global health challenge, with a high rate of infection and mortality. Chest X-ray (CXR) imaging is a common diagnostic tool for TB, but its effectiveness depends heavily on the expertise of the radiologist. This study explores the impact of image preprocessing on the performance of deep learning models in TB detection from CXR images, evaluating whether the computational cost of preprocessing is justified compared to using non-preprocessed images. A combination of all these preprocessing techniques was applied on the dataset, including Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform, gamma correction, and histogram equalization, as provided by the dataset itself. The results indicate that preprocessing enhanced the accuracy of the ResNet50 model significantly, achieving 99% accuracy compared to 94% on raw images. However, for MobileNet and the custom model, the improvement was marginal, suggesting that these models can perform adequately without extensive preprocessing. This finding highlights the potential for implementing deep learning models in low-resource settings where computational capabilities are limited. The study underscores the importance of selecting appropriate preprocessing techniques and neural network architectures to optimize TB detection accuracy in diverse clinical environments.

autores

http://vivoweb.org/ontology/core#FacultyMember
Orjuela Cañón, Alvaro David

fecha de publicación

2025-1-1

Área temática

Salud y bienestar

Identificador de objeto digital (DOI)

https://doi.org/10.1007/978-3-031-82123-3_5

Número Estándar Internacional de Libros (ISBN)

9783031821226

Número de páginas

12

Página inicial

53

Última página

64