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Assessment of Machine Learning Algorithms for the Purpose of Primary Sjögren’s Syndrome Grade Classification from Segmented Ultrasonography Images

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 241)

Abstract

Primary Sjögren’s syndrome (pSS) is a chronic autoimmune disease that affects primarily women (9 females/1 male). Recently, a great interest has arisen for salivary gland ultrasonography (SGUS) as a valuable tool for the assessment of major salivary gland involvement in primary Sjögren’s syndrome. The aim of this study was to assess accuracy of state of the art machine learning algorithms for the purpose of classifying pSS from SGUS images. The five-step procedure was carried out, including: image pre- processing, feature extraction, data set balancing and feature extraction, classifiers (K-Nearest Neighbour, Decision trees, Naive bayes, Discriminant analysis classifier, Random forest, Multilayer perceptron, Linear logistic regression) learning and their corresponding assessment. The preliminary results on the growing HarmonicSS cohort showed that Naive bayes (72.8% accuracy on training set, and 73.3% accuracy on test set) and Multilayer perceptron (85.0% accuracy in training stage, and 70.1% accuracy at test stage) are the most suitable for the purpose of pSS grade classification.

Keywords

Sjögren’s syndrome Classification Ultrasonography 

Notes

Acknowledgments

This study was funded by the grants from the Serbia III41007, ON174028 and EC HORIZON2020 HarmonicSS project.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.BioIRC, Bioengineering Research and Development CenterKragujevacSerbia
  2. 2.Faculty of EngineeringUniversity of KragujevacKragujevacSerbia
  3. 3.Azienda Ospedaliero Universitaria, Santa Maria Della Misericordia di UdineUdineItaly

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