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Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs

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Julia Werner, Christoph Gerum, Moritz Reiber, Jörg Nick, Oliver Bringmann

Machine Learning · Submitted 11 Oct 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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

how it is stated · asserted 0.95 read the record →

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arXiv ID2310.07895 (version 2)
Pith IDBXHAPW3C, Pith's permanent record of this paper and its review
CitationChristoph Gerum, J\"org Nick, Julia Werner, Moritz Reiber, Oliver Bringmann. "Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs." arXiv:2310.07895 (2023). https://pith.science/paper/BXHAPW3C
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,656 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.17 · human participants 0.68 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.09 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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). contribution 0.79 0.86 0.14 asserted 0.99
s1 It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. result 0.68 0.89 0.15 asserted 1.00
s2 Our approach achieves an accuracy of $98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. result 1.00 0.96 0.21 asserted 1.00
s3 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 interpretation 0.82 0.88 0.46 asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.48.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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Uses the method or tool of 1 paper

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