ROC CURVE ANALYSIS OF DIFFERENT HYBRID FEATURE DESCRIPTORS USING MULTI CLASSIFIERS

Abstract

Tremendous success of machine learning algorithms at pattern recognition creates interest in new inventions. Machine learning in an era of big data is that significant hierarchical relationships within the data can be discovered algorithmically than other handcraft like features. In this study, Convolutional Neural Network (CNN) is used as feature descriptors in pulmonary malignancy prediction. Various feature descriptors such as Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradient (EXHOG) and Linear Binary Pattern (LBP) descriptors are analyzed with classifiers such as Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for Computed Tomography (CT) The phenotype features of pulmonary nodules are important cues for identification. The nodule solidity is an important cue for white blob area identification. The method is analyzed in Lung Image Database Consortium (LIDC) dataset. Receivers Operating Characteristics (ROC) curves show the graphical summaries of detectors performance. It is proved that CNN based feature extraction with SVM classifier works well in pulmonary malignancy prediction.