Paper ID: 2310.07895

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

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

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

Submitted: Oct 11, 2023