Clustering-Based Undersampling to Support Automatic Detection of Focal Cortical Dysplasias
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.
KeywordsImbalance learning Clustering Bagging
This research is developed under the project Desarrollo de un sistema de apoyo al diagnóstico no invasivo de pacientes con epilepsia farmacoresistente asociada a displasias corticales cerebrales: Método costo-efectivo basado en procesamiento de imágenes de resonancia magnética, financed by COLCIENCIAS with code 111074455778. Thanks for the support to the master in electrical engineering program of the Universidad Tecnológica de Pereira.
- 3.Ahmed, B., Brodley, C.E., Blackmon, K.E., Kuzniecky, R., Barash, G., Carlson, C., Quinn, B.T., Doyle, W., French, J., Devinsky, O., et al.: Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia. Epilepsy Behav. 48, 21–28 (2015)CrossRefGoogle Scholar
- 9.Wallace, B.C., Small, K., Brodley, C.E., Trikalinos, T.A.: Class imbalance, redux. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 754–763. IEEE (2011)Google Scholar