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Despite the progress made in the field of medical imaging, it remains a largearea of open research, especially due to the variety of imaging modalities anddisease-specific characteristics. This paper is a comparative study describingthe potential of using local binary patterns (LBP), deep features and thebag-of-visual words (BoVW) scheme for the classification of histopathologicalimages. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960histopathology images belonging to 20 different classes (different tissuetypes). We make this dataset publicly available. The small size of the datasetand its inter- and intra-class variability makes it ideal for initialinvestigations when comparing image descriptors for search and classificationin complex medical imaging cases like histopathology. We investigate deepfeatures, LBP histograms and BoVW to classify the images via leave-one-outvalidation. The accuracy of image classification obtained using LBP was 90.62\%while the highest accuracy using deep features reached 94.72\%. The dictionaryapproach (BoVW) achieved 96.50\%. Deep solutions may be able to deliver higheraccuracies but they need extensive training with a large number of (balanced)image datasets.

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