One-class classification using extreme learning machine with subspace feature mapping
Date of Issue2014
School of Electrical and Electronic Engineering
This final year project proposes Random Feature Subspace Ensemble based Extreme Learning Machine (RFSE-ELM) classifier to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients’ CT data and experiment shows promising results. Part of the progress of this final year project was written as a conference paper submitted to 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14) to be held at the Sheraton Hotel & Towers, Chicago, Illinois, USA from August 26-30, 2014.
Final Year Project (FYP)
Nanyang Technological University