Counting of white blood cells (WBCs) and detecting the morphological abnormality of these cells allow for diagnosis some blood diseases such as leukemia. This can be accomplished by automatic quantification analysis of microscope images of blood smear. This paper is oriented towards presenting a novel framework that consists of two sub-systems as indicators for detection Acute Lymphoblastic Leukemia (ALL). The first sub-system aims at counting WBCs by adapting a deep learning based approach to separate agglomerates of WBCs. After separation of WBCs, we propose the second sub-system to detect and count abnormal WBCs (lymphoblasts) required to diagnose ALL. The performance of the proposed framework is evaluated using ALL-IDB dataset. The first presented sub-system is able to count WBCs with an accuracy up to 97.38%. Furthermore, an approach using ensemble classifiers based on handcrafted features is able to detect and count the lymphoblasts with an average accuracy of 98.67%.
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