A CONVOLUTIONAL NEURAL NETWORK APPROACH FOR ABNORMALITY DETECTION IN WIRELESS CAPSULE ENDOSCOPY

Sekuboyina, Anjany Kumar and Devarakonda, Surya Teja and Seelamantula, Chandra Sekhar (2017) A CONVOLUTIONAL NEURAL NETWORK APPROACH FOR ABNORMALITY DETECTION IN WIRELESS CAPSULE ENDOSCOPY. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, APR 18-21, 2017, pp. 1057-1060.

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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 paper, 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.

Item Type:	Conference Proceedings
Series.:	Melbourne, AUSTRALIA
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	23 Dec 2017 08:31
Last Modified:	23 Dec 2017 08:31
URI:	http://eprints.iisc.ac.in/id/eprint/58532

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