A hybrid descriptor to improve kidney pathologies classification
Authors: 
Laiara Cristina da Silva, Vinícius Ponte Machado, Rodrigo de Melo Sousa Veras, Keylla Maria de Sá Urtiga Aita, Semiramis Jamil Hadad do Monte, Nayze Lucena Sangreman AldemanAuthors Info & Claims
Pages 653 - 659
Abstract
The importance of glomerular function in kidney physiology characterizes glomerular diseases as the main problem in nephrology. So finding and classifying glomerular disorders are fundamental steps for diagnosing many kidney diseases. This paper conducted an extensive study to determine the best set of features for glomerular image representation. Our feature extraction methodology, which includes clinical data, texture, and global descriptors, resulted in 8486 features. Besides, we compared four classifiers to propose a method that helps the specialist define a renal pathology diagnosis. The proposed method achieved an accuracy of 98.46% and a Kappa index of 98.42% using the Random Forest Classifier. We concluded that a combination of clinical data and global image features facilitates accurate disease classification.
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- A hybrid descriptor to improve kidney pathologies classification
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Published In
April 2022
2099 pages
ISBN:9781450387132
DOI:10.1145/3477314
- Conference Chairs:
- Jiman Hong,
- Miroslav Bures,
- Program Chairs:
- Juw Won Park,
- Tomas Cerny
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Published: 06 May 2022
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