An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images

  • Pedro Pedrosa Rebouças Filho IFCE
  • Suane Pires Pinheiro da Silva IFCE
  • Jefferson Silva Almeida IFCE
  • Elene Firmeza Ohata IFCE
  • Shara Shami Araujo Alves IFCE
  • Francisco dos Santos Hercules Silva IFCE

Resumo


Chronic kidney diseases cause over a million deaths worldwide every year. One of the techniques used to diagnose the diseases is renal scintigraphy. However, the way that is processed can vary depending on hospitals and doctors, compromising the reproducibility of the method. In this context, we propose an approach to process the exam using computer vision and machine learning to classify the stage of chronic kidney disease. An analysis of different features extraction methods, such as Gray-Level Co-occurrence Matrix, Structural Co-occurrence Matrix, Local Binary Patters (LBP), Hu's Moments and Zernike's Moments in combination with machine learning methods, such as Bayes, Multi-layer Perceptron, k-Nearest Neighbors, Random Forest and Support Vector Machines (SVM), was performed. The best result was obtained by combining LBP feature extractor with SVM classifier. This combination achieved accuracy of 92.00% and F1-score of 91.00%, indicating that the proposed method is adequate to classify chronic kidney disease in two stages, being a high risk of developing end-stage renal failure and other outcomes, and otherwise.

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Publicado
28/10/2019
REBOUÇAS FILHO, Pedro Pedrosa; DA SILVA, Suane Pires Pinheiro; ALMEIDA, Jefferson Silva; OHATA, Elene Firmeza; ALVES, Shara Shami Araujo; HERCULES SILVA, Francisco dos Santos. An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 156-159. DOI: https://doi.org/10.5753/sibgrapi.est.2019.8318.

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