Clustering-based undersampling to support automatic detection of focal cortical dysplasias

Abstract

Focal Cortical Dysplasias (FCDs) are cerebral cortex abnormalities that cause epileptic seizures. Recently, machine learning techniques have been developed to detect FCDs automatically. However, dysplasias datasets contain substantially fewer lesional samples than healthy ones, causing high order imbalance between classes that affect the performance of machine learning algorithms. Here, we propose a novel FCD automatic detection strategy that addresses the class imbalance using relevant sampling by a clustering strategy approach in cooperation with a bagging-based neural network classifier. We assess our methodology on a public FCDs database, using a cross-validation scheme to quantify classifier sensitivity, specificity, and geometric mean. Obtained results show that our proposal achieves both high sensitivity and specificity, improving the classification performance in FCD detection in comparison to the state-of-the-art methods.