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

arXiv: https://arxiv.org/abs/2310.07895 · HTML: https://mlsanity.com/paper/2310.07895/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices](https://mlsanity.com/paper/2209.03807/) (2022)

## Built on by (most cited first, 5 of 5)

- [Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation](https://mlsanity.com/paper/2507.23398/) (2025)
- [Energy-Efficient Seizure Detection Suitable for low-power Applications](https://mlsanity.com/paper/2406.16948/) (2024)
- [Seeing More with Less: Video Capsule Endoscopy with Multi-Task Learning](https://mlsanity.com/paper/2507.23479/) (2025)
- [Reliable Mislabel Detection for Video Capsule Endoscopy Data](https://mlsanity.com/paper/2602.06938/) (2026)
- [CapsuleMotion: A Lightweight Real-Time Visual Motion Predictor for Capsule Endoscopy](https://mlsanity.com/paper/2609.15367/) (2026)

## Abstract (arXiv)

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