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High accuracy patch-level classification of wireless capsule endoscopy images using a convolutional neural network

Sadasivan, VS and Seelamantula, CS (2019) High accuracy patch-level classification of wireless capsule endoscopy images using a convolutional neural network. In: 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, 8 - 11 April 2019, Venice, pp. 96-99.

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Official URL: https://doi.org/10.1109/ISBI.2019.8759324


Wireless capsule endoscopy (WCE) is a technology used to record colored internal images of the gastrointestinal (GI) tract for the purpose of medical diagnosis. It transmits a large number of frames in a single examination cycle, which makes the process of analyzing and diagnosis of abnormalities extremely challenging and time-consuming. In this paper, we propose a technique to automate the abnormality detection in WCE images following a deep learning approach. The WCE images are split into patches and input to a convolutional neural network (CNN). A trained deep neural network is used to classify patches to be either malign or benign. The patches with abnormalities are marked on the WCE image output. We obtained an area under receiver-operating-characteristic curve (AUROC) value of about 98.65 on a publicly available test data containing nine abnormalities. © 2019 IEEE.

Item Type: Conference Paper
Publication: Proceedings - International Symposium on Biomedical Imaging
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society
Keywords: Classification (of information); Convolution; Deep learning; Endoscopy; Image classification; Medical imaging; Neural networks, Abnormality detection; Convolutional neural network; Gastrointestinal tract; Internal images; Learning approach; Receiver operating characteristic curves; Wireless capsule endoscopy; Wireless capsule endoscopy image, Deep neural networks
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 13 Dec 2022 05:14
Last Modified: 13 Dec 2022 05:14
URI: https://eprints.iisc.ac.in/id/eprint/78338

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