  Recent advances in digital imaging has transformed computer vision and
machine learning to new tools for analyzing pathology images. This trend could
automate some of the tasks in the diagnostic pathology and elevate the
pathologist workload. The final step of any cancer diagnosis procedure is
performed by the expert pathologist. These experts use microscopes with high
level of optical magnification to observe minute characteristics of the tissue
acquired through biopsy and fixed on glass slides. Switching between different
magnifications, and finding the magnification level at which they identify the
presence or absence of malignant tissues is important. As the majority of
pathologists still use light microscopy, compared to digital scanners, in many
instance a mounted camera on the microscope is used to capture snapshots from
significant field-of-views. Repositories of such snapshots usually do not
contain the magnification information. In this paper, we extract deep features
of the images available on TCGA dataset with known magnification to train a
classifier for magnification recognition. We compared the results with LBP, a
well-known handcrafted feature extraction method. The proposed approach
achieved a mean accuracy of 96% when a multi-layer perceptron was trained as a
classifier.
