K-Means system and SIFT algorithm as a faster and more efficient solution for the detection of pulmonary tuberculosis
Título del documento: K-Means system and SIFT algorithm as a faster and more efficient solution for the detection of pulmonary tuberculosis
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560543
ISSN: 1405-5546
Autores: 1
1
2
1
Instituciones: 1Universidad Nacional Mayor de San Marcos, Lima. Perú
2Universidad Nacional Amazónica de Madre de Dios, Madre de Dios. Perú
Año:
Periodo: Jul-Sep
Volumen: 24
Número: 3
Paginación: 989-997
País: México
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés Tuberculosis is a lethal disease that attacks the lungs in a similar way to COVID 19, according to the who, until 2018 there were more than 10 million people infected with tuberculosis and 1.5 million died with this disease. Artificial Intelligence algorithms allow detecting these diseases quickly and massively. We present an architecture to detect tuberculosis with image processing on lung radiographs, using the SIFT and K-means algorithms. We have tested the architecture with 300 radiographs, achieving 90.3% accuracy in classification.
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial
Keyword: Image processing,
K-Means,
SIFT algorithm,
Machine learning,
Artificial intelligence
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