Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy

TL;DR

This paper introduces a deep learning approach using a Havrda-Charvat entropy-based loss function for classifying informative and uninformative pulmonary endomicroscopy images, improving stability and reducing overfitting.

Contribution

It develops a novel CNN classifier with a Havrda-Charvat entropy loss function tailored for pulmonary endomicroscopy image classification, outperforming Shannon entropy-based methods.

Findings

Better classification accuracy than Shannon entropy methods

Enhanced model stability and reduced overfitting

Effective on a dataset of 2947 images

Abstract

Pulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have as much as 25% of the sequence being uninformative frames (i.e. pure-noise and motion artefacts). For future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images. We propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy which is a parametrical generalization of the Shannon entropy. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy. Our method is tested on one POE dataset including 2947 distinct images, is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting. Keywords: Deep Learning, CNN, Shannon entropy, Havrda-Charvat entropy, Pulmonary optical endomicroscopy.