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.
Highlights
Feature design for image recognition has been studied for decades
This paper focuses on the problem of feature extraction and the classification of microvascular morphological types to aid esophageal cancer detection
This paper focuses on the recognition of intraepithelial papillary capillary loops (IPCLs), a kind of esophageal microvessel, whose types are closely related to the depth of tumor invasion of esophageal squamous cell carcinoma
Summary
Feature design for image recognition has been studied for decades Powerful features, such as local binary pattern (LBP) [1], scale-invariant feature transform (SIFT) [2], speeded up robust features (SURF) [3], and histograms of oriented gradients (HOG) [4], have been proposed to promote the development of classical computer vision and pattern recognition tasks. These traditional handcrafted features are unsatisfactory for distinctive tasks, especially medical image processing. Type B1 dilated and tortuous vessels of various diameters and shapes and with intact loop formation
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