Premium
Classification of acute lymphoblastic leukemia using deep learning
Author(s) -
Rehman Amjad,
Abbas Naveed,
Saba Tanzila,
Rahman Syed Ijaz ur,
Mehmood Zahid,
Kolivand Hoshang
Publication year - 2018
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23139
Subject(s) - bone marrow , lymphoblastic leukemia , convolutional neural network , artificial intelligence , medicine , deep learning , acute leukemia , leukemia , segmentation , pattern recognition (psychology) , computer science , machine learning , pathology
Abstract Acute Leukemia is a life‐threatening disease common both in children and adults that can lead to death if left untreated. 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 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 thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. 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.