A hybrid convolutional neural networks with extreme learning machine for WCE image classification

Wireless Capsule Endoscopy (WCE) is considered as a promising technology for non-invasive gastrointestinal disease examination. This paper studies the classification problem of the digestive organs for wireless capsule endoscopy (WCE) images aiming at saving the review time of doctors. Our previous study has proved the Convolutional Neural Networks (CNN)-based WCE classification system is able to achieve 95% classification accuracy in average, but it is difficult to further improve the classification accuracy owing to the variations of individuals and the complex digestive tract circumstance. Research shows that there are two possible approaches to improve classification accuracy: to extract more discriminative image features and to employ a more powerful classifier. In this paper, we propose to design a WCE classification system by a hybrid CNN with Extreme Learning Machine (ELM). In our approach, we construct the CNN as a data-driven feature extractor and the cascaded ELM as a strong classifier instead of the conventional used full-connection classifier in deep CNN classification system. Moreover, to improve the convergence and classification capability of ELM under supervision manner, a new initialization is employed. Our developed WCE image classification system is named as HCNN-NELM. With about 1 million real WCE images (25 examinations), intensive experiments are conducted to evaluate its performance. Results illustrate its superior performance compared to traditional classification methods and conventional CNN-based method, where about 97.25% classification accuracy can be achieved in average.