A novel binary patterns approach on chest radiographs to advance tuberculosis diagnosis
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1349Keywords:
Diagnosis, Artificial intelligence, TuberculosisAbstract
Objective: Tuberculosis (TB) affects millions of people, especially the most miserable, revealing social inequalities. Despite advances in artificial intelligence (AI) in TB control, few benefits reach those most in need. This study proposes an optimized AI to discriminate TB cases from healthy individuals. Method: The approach incorporates phase congruence descriptors and local binary patterns into a sequential minimum optimization (SMO) model to analyze chest radiographs (CXR). Results: The optimized AI performs better than existing approaches in the literature, delivering a specificity value greater than 97% in different bases and segmentation scenarios. Conclusion: Applying the proposed AI in RXT analysis could represent a significant advance in TB control, especially in populations most in need, as it constitutes an accessible and effective solution that opens up possibilities for developing new diagnostic support systems.
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