Open AccessA Convolutional Neural Network Based Disease Diagnosis in Wireless Capsule Endoscopy Images
Author(s) -
Mrs.R. Sathiya*,
Dr.R.Kalai Magal
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8515.109119
Subject(s) - capsule endoscopy , artificial intelligence , computer science , convolutional neural network , pattern recognition (psychology) , computer vision , classifier (uml) , abnormality , feature extraction , receiver operating characteristic , radiology , medicine , machine learning , psychiatry
In wireless capsule endoscopy (WCE), a swallow-able miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert’s time to review the scan. In this research, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches and extract features pertaining to each block using a Convolutional neural network (CNN) to increase their generality while overcoming the drawbacks of manually crafted features. We intend to exploit the importance of color information for the task. Experiments are performed to determine the optimal color space components for feature extraction and classifier design. We obtained an area under receiver-operating-characteristic (ROC) curve of approximately 0.8 on a dataset containing multiple abnormalities.