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.

A survey on deep learning in medical image analysis

“…Several researchers have shown that designing architectures incorporating unique task-specific properties can obtain better results than straightforward CNNs. Two examples which we encountered several times are multi-view Gao et al (2016d) Frame labeling US CNN 4 class frame classification using transfer learning with pre-trained networks Kumar et al (2016) Frame labeling US CNN 12 standard anatomical planes, CNN extracts features for support vector machine Rajchl et al (2016b) Segmentation with non expert labels MRI CNN Crowd-sourcing annotation efforts to segment brain structures Rajchl et al (2016a) Segmentation given bounding box MRI CNN CNN and CRF for segmentation of structures Ravishankar et al (2016a) Quantification US CNN Hybrid system using CNN and texture features to find abdominal circumference Yu et al (2016b) Left ventricle segmentation US CNN Frame-by-frame segmentation by dynamically fine-tuning CNN to the latest frame Wound segmentation photographs CNN Additional detection of infection risk and healing progress Ypsilantis et al (2015) Chemotherapy response prediction PET CNN CNN outperforms classical radiomics features in patients with esophageal cancer Zheng et al (2015) Carotid artery bifurcation detection CT CNN Two stage detection process, CNNs combined with Haar features Alansary et al (2016) Placenta segmentation MRI CNN 3D multi-stream CNN with extension for motion correction Fritscher et al (2016) Head&Neck tumor segmentation CT CNN 3 orthogonal patches in 2D CNNs, combined with other features Jaumard- Hakoun et al (2016) Tongue contour extraction US RBM Analysis of tongue motion during speech, combines auto-encoders with RBMs Payer et al (2016) Hand landmark detection X-ray CNN Various architectures are compared Quinn et al (2016) Disease detection microscopy CNN Smartphone mounted on microscope detects malaria, tuberculosis & parasite eggs Smistad and Løvstakken (2016) Vessel detection and segmentation US CNN Femoral and carotid vessels analyzed with standard fCNN Twinanda et al (2017) Task recognition in laparoscopy Videos CNN Fine-tuned AlexNet applied to video frames Xu et al (2016c) Cervical dysplasia cervigrams CNN Fine-tuned pre-trained network with added non-imaging features Xue et al (2016) Esophageal microvessel classification Microscopy CNN Simple CNN used for feature extraction Zhang et al (2016a) Image reconstruction CT CNN Reconstructing from limited angle measurements, reducing reconstruction artefacts Lekadir et al (2017) Carotid plaque classification US CNN Simple CNN for characterization of carotid plaque composition in ultrasound …”

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

“…CNNs have been exploited in [157] to extract esophageal microvessel features from NBI microscopy. The extracted features are then classified with SVM.…”

Sensor‐based activity recognition independent of device placement and orientation

“…This causes bad performance on generalization of the model. Another limitation is that, when the data flow comes to the output layer, CNN will tend to assign a huge value (nearly 1) to one class while other classes get a tiny value (nearly 0), which largely reduces the fault tolerance of CNN in the practical scenario . Considering this, we use a hybrid structure for classification.…”