An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

International Journal of Biomedical Imaging
Mustain BillahMohammad Motiur Rahman

Abstract

Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.

References

Sep 19, 2019·Expert Review of Medical Devices·Jiangchang XuXiaojun Chen
Mar 13, 2020·PloS One·Michelle ViscainoFernando Auat Cheein
Jan 22, 2020·Current Treatment Options in Gastroenterology·Nicholas HoerterPeter S Liang
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Related Concepts

Endoscopy (Procedure)
Biological Neural Networks
Diagnostic Radiology Modality
Polyps
Evaluation
Neural Network Simulation
Video Media
Malignant Neoplasms
Video Imaging
Capsule Endoscopy

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