Diagnosing Tuberculosis with Artificial Neural Networks and BPPC Features
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1106Keywords:
Tuberculosis, Neural Networks, Pattern RecognitionAbstract
Tuberculosis is a serious and contagious disease that kills millions of people, being a global public health problem. Meanwhile, Artificial Intelligence in radiology has aroused increasing interest from researchers and the industry. Solutions to assist in diagnosis are already a reality, but distant from vulnerable populations and underdeveloped regions. Therefore, accessible solutions are essential for people that are highly dependent on public actions and services. Thus, we propose a method of low computational cost and high efficiency to aid in diagnosing tuberculosis. We used chest radiography images and built an artificial neural network model with BPPC features with and without synthetic data generation. The results comparable to the literature show the solution's exceptional performance and low cost, placing it as a viable alternative solution.
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Copyright (c) 2023 Afonso Ueslei Fonseca, Juliana Paula Felix, Gabriel Silva Vieira, Bruno Moraes Rocha, Emília Alves Nogueira, Carlos Eduardo Egito Araújo, Deborah Fernandes, Fabrizzio Soares
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