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

arXiv: https://arxiv.org/abs/1805.05837 · HTML: https://mlsanity.com/paper/1805.05837/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

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

- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis](https://mlsanity.com/paper/2301.01931/) (2023)
- [MIAFEx: An Attention-based Feature Extraction Method for Medical Image Classification](https://mlsanity.com/paper/2501.08562/) (2025)
- [Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories](https://mlsanity.com/paper/2104.10762/) (2021)
- [Hand Gesture Recognition Based on a Nonconvex Regularization](https://mlsanity.com/paper/2104.14349/) (2021)

## Abstract (arXiv)

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%.