A convolutional neural network based disease diagnosis in wireless capsule endoscopy images

0Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In wireless capsule endoscopy (WCE), a swallowable 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.

Cite

CITATION STYLE

APA

Sathiya, R., & Kalaimagal, R. (2019). A convolutional neural network based disease diagnosis in wireless capsule endoscopy images. International Journal of Engineering and Advanced Technology, 9(1), 327–331. https://doi.org/10.35940/ijeat.F8515.109119

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free