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 in the bone marrow. Acute Leukemia is common in both children and adults. It can also cause death if left untreated. Hematologists diagnose ALL by examining the blood and bone marrow. This method used is slow and takes more time. In this study, the diagnosis and classification of the disease was carried out using peripheral smear images with the proposed method. In the proposed method, 99.80% accuracy was obtained by using the DarkNet19 pre-trained model. Then, 1000 features were obtained from Darknet19. 521 of the obtained features were selected with Mrmr feature selection algorithm. The selected features are classified with support vector machines. An accuracy of 99.94% was achieved with the proposed method. The results show that the proposed method can be used as a tool that will certainly assist pathologists in diagnosing ALL and its subtypes.