Performance Analysis of ELM Classifier for Classification of Electrical Impedance Tomography (EIT) Images with Infinite Feature Selection

This article presents the performance analysis of Infinite Feature Selection (Inf-FS)-Extreme Learning Machine (ELM) classifier for detecting the abnormalities from Electrical impedance tomography images. The machine learning algorithm, Extreme Learning Machine (ELM), is used for classification and a Infinite Feature Selection is used as feature selector to reduce the high dimensional features needed for classification. The Gray Level Co-occurrence Matrix (GLCM) and intensity histogram are used for texture feature extraction from the EIT images. The EIT lung images are reconstructed using one step linearized Gauss-Newton (GN) algorithm. Detection of lung injury is one of the critical issues where excessive care has to be taken for better diagnosis and treatment. The performance analysis of this classifier is analyzed based on the benchmark parameters such performance index, sensitivity, specificity, average detection and F-score. From the experimental results it is evident, that the Extreme Learning Machine has performed well compared with the Support Vector Machine.