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

LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer