Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
This enables precise localization in the GI tract for medical diagnostics, with a method suitable for low-power devices, but it is incremental as it builds on existing CNN and HMM techniques.
The paper tackles the problem of classifying gastroenterologic sections from Video Capsule Endoscopy images by combining a CNN for classification with an HMM for time-series analysis to correct errors, achieving 98.04% accuracy on the Rhode Island dataset.
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