%0 Journal Article %T A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules %J Asian Pacific Journal of Cancer Prevention %I West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. %Z 1513-7368 %A Kailasam, S Piramu %A Sathik, M Mohamed %D 2019 %\ 02/01/2019 %V 20 %N 2 %P 457-468 %! A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules %K Deep Learning %K convolutional neural network %K Feature Descriptor %K Classification %R 10.31557/APJCP.2019.20.2.457 %X In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on earlydiagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network(DCNN) method is used for feature extraction and hybridize as combination of Convolutional Neural Network (CNN),Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradients (ExHOG) and Local Binary Pattern(LBP). A combination of shape, texture, scaling, rotation, translation features extracted using HOG, LBP and CNN. TheHomogeneous descriptors used to extract the feature of lung images from Lung Image Database Consortium (LIDC)are given to classifiers Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree and RandomForest to classify nodules and non-nodules. Experimental results demonstrate the effectiveness of the proposed methodin terms of accuracy which gives best result than the competing methods. %U https://journal.waocp.org/article_82233_311b9bdfc6390e548b1689f28b4cf2c2.pdf