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Transfer learning based video summarization in wireless capsule endoscopy

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Abstract

Wireless capsule endoscopy (WCE) is a noninvasive procedure to examine gastrointestinal tract. The main challenge in this medical procedure is the time required to examine the recorded video by the medical expert. The computer aided diagnosis of gastrointestinal disorders can prove great help in WCE examination procedure. In this paper a WCE video summarization technique is proposed which makes use of transfer learning. In the methodology, Inception V3, a version of standard convolution neural network (CNN) architecture is used for transfer learning along with K-means clustering. The experimental results are evaluated by using F-measure and compression ratio. The results depicts that the Transfer Learning based Video Summarization (TLVS) method performs well in eliminating redundant frames and thus ultimately reduces the time of diagnosis in WCE video inspection.

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Data availability

KID datasets.

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Software application.

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Acknowledgements

We thank Dr. Dimitris Iakovidis, University of Thessaly, Greece for permitting access to the requested KID datasets.

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  1. Electronics and Communication Engineering Department, MIT ADT University, Pune, India

    Vrushali Raut & Reena Gunjan

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  1. Vrushali Raut
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  2. Reena Gunjan
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Correspondence to Vrushali Raut.

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Raut, V., Gunjan, R. Transfer learning based video summarization in wireless capsule endoscopy. Int. j. inf. tecnol. 14, 2183–2190 (2022). https://doi.org/10.1007/s41870-022-00894-0

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