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An estimated 300,000 new cases of leukemia are diagnosed each year which is2.8 percent of all new cancer cases and the prevalence is rising day by day.The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia(ALL), which affects people of all age groups, including children and adults.In this study, we propose an automated system to detect various-shaped ALLblast cells from microscopic blood smears images using Deep Neural Networks(DNN). The system can detect multiple subtypes of ALL cells with an accuracy of98 percent. Moreover, we have developed a telediagnosis software to providereal-time support to diagnose ALL subtypes from microscopic blood smearsimages.

Automated Detection of Acute Lymphoblastic Leukemia Subtypes from Microscopic Blood Smear Images using Deep Neural Networks