Tuberculosis Detection Comparison by Using Preprocessed and Non-preprocessed Chest X-Ray Images

Rubén Saúl Jiménez-Fernández, Axel Yahir Ramírez-Ángel, Alvaro David Orjuela-Cañón

Research output: Chapter in Book/InformConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication47th Mexican Conference on Biomedical Engineering - Proceedings of CNIB 2024 - Signal Processing And Bioinformatics Congreso Nacional de Ingeniería Biomédica CNIB Hermosillo
EditorsJosé de Jesús Agustín Flores Cuautle, Balam Benítez-Mata, José Javier Reyes-Lagos, Humiko Yahaira Hernandez Acosta, Gerardo Ames Lastra, Esmeralda Zuñiga-Aguilar, Edgar Del Hierro-Gutierrez, Ricardo Antonio Salido-Ruiz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages53-64
Number of pages12
ISBN (Print)9783031821226
DOIs
StatePublished - 2025
Event47th Mexican Conference on Biomedical Engineering, CNIB 2024 - Hermosillo, Mexico
Duration: Nov 7 2024Nov 9 2024

Publication series

NameIFMBE Proceedings
Volume116 IFMBE
ISSN (Print)1680-0737
ISSN (Electronic)1433-9277

Conference

Conference47th Mexican Conference on Biomedical Engineering, CNIB 2024
Country/TerritoryMexico
CityHermosillo
Period11/7/2411/9/24

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

Cite this