Comparing LBP, HOG and Deep Features for Classification of Histopathology Images

TL;DR

This study compares local binary patterns, histogram of gradients, and deep features for classifying histopathology images, finding LBP features with SVM outperform deep features in accuracy.

Contribution

It provides a comparative analysis of traditional and deep feature extraction methods for histopathology image classification using multiple classifiers.

Findings

LBP features with SVM achieve 90.52% accuracy

Deep features achieve 81.14% accuracy

LBP outperforms deep features in this dataset

Abstract

Medical image analysis has become a topic under the spotlight in recent years. There is a significant progress in medical image research concerning the usage of machine learning. However, there are still numerous questions and problems awaiting answers and solutions, respectively. In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network. Three common image classification methods, including support vector machines, decision trees, and artificial neural networks are used to classify feature vectors obtained by different feature extractors. We use KIMIA Path960, a publicly available dataset of $960$ histopathology images extracted from $20$ different tissue scans to test the accuracy of classification and feature extractions models used in the study, specifically for the histopathology images. SVM achieves the highest accuracy of $90.52\%$ using local binary patterns as features which surpasses the accuracy obtained by deep features, namely $81.14\%$.