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

TL;DR

This study compares LBP, deep features, and BoVW for classifying histopathological images, introducing a new dataset and demonstrating that BoVW achieves the highest accuracy among the methods tested.

Contribution

It introduces the KIMIA Path960 dataset and evaluates the effectiveness of LBP, deep features, and BoVW for histopathology image classification.

Findings

LBP achieved 90.62% accuracy

Deep features reached 94.72% accuracy

BoVW achieved 96.50% accuracy

Abstract

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, \emph{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.