Open AccessImpact of Feature Selection on Clustering Images ofVertebral Compression Fractures
Author(s) -
Raquel Mariana Candido,
Rafael Del Lama,
Natália Chiari,
Marcello Henrique Nogueira-Barbosa,
Paulo Azevedo Marques,
Renato Tinós
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12176
Subject(s) - cluster analysis , computer science , feature selection , artificial intelligence , selection (genetic algorithm) , pattern recognition (psychology) , feature (linguistics) , compression (physics) , machine learning , philosophy , linguistics , materials science , composite material
Non-traumatic Vertebral Compression Fractures (VCFs) are generally caused by osteoporosis (benign VCFs) or metastatic cancer (malignant VCFs) and the success of the medical treatment strongly depends on a fast and correct classification of VCFs. Recently, methods for computer-aided diagnosis (CAD) based on machine learning have been proposed for classifying VCFs. In this work, we investigate the problem of clustering images of VCFs and the impact of feature selection by genetic algorithms, comparing the clustering i)with all features and ii)with feature selection through the purity results. The analysis of the clusters helps to understand the results of classifiers and difficulties of differentiating images of different classes by an expert. The results indicate that features selection improved the separability of clusters and purity. Feature selection also helps to understand which attributes are most important for analysing the images of vertebral bodies.