K-Means system and SIFT algorithm as a faster and more efficient solution for the detection of pulmonary tuberculosis

Luis M. Ortega Melgarejo, Hugo David Calderon Vilca, Guido Raul Larico Uchamaco, Flor Caniy Cárdenas Mariño


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 to detect 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.


Image processing, K-Means, SIFT algorithm, machine learning, artificial intelligence

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