Detection of acute lymphocytic leukemia (ALL) with a pre-trained deep learning model

Abstract

Acute Lymphocytic Leukemia (ALL) is a type of cancer caused by immature lymphocytes inthe bone marrow. Acute Leukemia is common in both children and adults. It can also cause death if leftuntreated. Hematologists diagnose ALL by examining the blood and bone marrow. This method used isslow and takes more time. In this study, the diagnosis and classification of the disease was carried out usingperipheral smear images with the proposed method. In the proposed method, 99.80% accuracy was obtainedby using the DarkNet19 pre-trained model. Then, 1000 features were obtained from Darknet19. 521 of theobtained features were selected with Mrmr feature selection algorithm. The selected features are classifiedwith support vector machines. An accuracy of 99.94% was achieved with the proposed method. The resultsshow that the proposed method can be used as a tool that will certainly assist pathologists in diagnosingALL and its subtypes.