Precise Localization Within the GI Tract by Combining Classification of CNNs and Time-Series Analysis of HMMs
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Author / Producer
Date
2024
Publication Type
Conference Paper
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yes
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Abstract
This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of 98.04% on the Rhode Island (RI) Gastroenterology dataset. This allows for precise localization within the gastrointestinal (GI) tract while requiring only approximately 1M parameters and thus, provides a method suitable for low power devices.
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Publication status
published
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Book title
Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part II
Journal / series
Volume
14349
Pages / Article No.
174 - 183
Publisher
Springer
Event
14th International Workshop on Machine Learning in Medical Imaging (MLMI 2023)
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Methods
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Subject
Medical Image Analysis; Wireless Capsule Endoscopy; GI Tract Localization