title: CNN-SVM for Microvascular Morphological Type Recognition with Data Augmentation.
abstract: This paper focuses on the problem of feature extraction and the classification of microvascular morphological types to aid esophageal cancer detection. We present a patch-based system with a hybrid SVM model with data augmentation for intraepithelial papillary capillary loop recognition. A greedy patch-generating algorithm and a specialized CNN named NBI-Net are designed to extract hierarchical features from patches. We investigate a series of data augmentation techniques to progressively improve the prediction invariance of image scaling and rotation. For classifier boosting, SVM is used as an alternative to softmax to enhance generalization ability. The effectiveness of CNN feature representation ability is discussed for a set of widely used CNN models, including AlexNet, VGG-16, and GoogLeNet. Experiments are conducted on the NBI-ME dataset. The recognition rate is up to 92.74% on the patch level with data augmentation and classifier boosting. The results show that the combined CNN-SVM model beats models of traditional features with SVM as well as the original CNN with softmax. The synthesis results indicate that our system is able to assist clinical diagnosis to a certain extent.
authors.affiliation: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027 China ; Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei, 230027 China.
authors.forename: Di-Xiu
authors.initials: DX
authors.lastname: Xue
authors.affiliation: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027 China ; Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei, 230027 China.
authors.forename: Rong
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China.
authors.forename: Hui
authors.initials: H
authors.lastname: Feng
authors.affiliation: Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China.
authors.forename: Ya-Lei
authors.initials: YL
authors.lastname: Wang
keywords: Convolutional neural network; Data augmentation; Feature representation; Microvascular type classification; Support vector machine (SVM)