Oct 25, 2017

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Younghak Shin, Ilangko Balasingham

Abstract

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

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Mentioned in this Paper

Support Vector Machines
Hand
Study
Biological Neural Networks
Physicians
Classification
Polyps
Colorectal Neoplasms
Colonoscopic Surgical Procedures
Neural Network Simulation

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