Detection and Classification of Acute Lymphocytic Leukemia

Abstract

Acute Leukemia is a life - threatening disease common both in children and adults that can lead to death if left un treated. Acute Lymphoblastic Leukemia (ALL) spreads out in children’s bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer –aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its sub types and reactive bone marrow (normal) in stained bone marrow images. Classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results reveal that the proposed method achieved more than 96% accuracy having four different classes. The obtained results exhibit that the proposed approach could be used as a tool to diagnose. Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.