# A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images

arXiv: https://arxiv.org/abs/1710.01249 · HTML: https://mlsanity.com/paper/1710.01249/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network](https://mlsanity.com/paper/2208.00594/) (2022)
- [Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings](https://mlsanity.com/paper/2102.07611/) (2021)
- [Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification](https://mlsanity.com/paper/2107.05940/) (2021)
- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)
- [Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware](https://mlsanity.com/paper/2606.21752/) (2026)
- [Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations](https://mlsanity.com/paper/2202.06073/) (2022)

## Abstract (arXiv)

Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, KIMIA Path960, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its inter- and intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62% while the highest accuracy using deep features reached 94.72%. The dictionary approach (BoVW) achieved 96.50%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.