  Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the
pediatric cancers. In general, the task of identifying immature leukemic blasts
from normal cells under the microscope is challenging because morphologically
the images of the two cells appear similar. In this paper, we propose a deep
learning framework for classifying immature leukemic blasts and normal cells.
The proposed model combines the Discrete Cosine Transform (DCT) domain features
extracted via CNN with the Optical Density (OD) space features to build a
robust classifier. Elaborate experiments have been conducted to validate the
proposed LeukoNet classifier.
