AUTOMATED ACUTE LYMPHOBLASTIC LEUKEMIA CELL CLASSIFICATION USING OPTIMIZED CONVOLUTIONAL NEURAL NETWORK

Abstract

Acute lymphoblastic leukemia (ALL) is the most common variant of paediatric cancer that creates numerous immature white blood cells affecting the bone marrow. Manual diagnosis of leukemia from microscopic evaluation of stained sample slides is an exhausting process, which is less accurate and susceptible to human errors. Additionally, identifying the leukemic blast cells under the microscope is complicated due to morphological similarity with the normal cell images. In this paper, we proposed an automated method to analyse the blood smear images using Local Binary Pattern (LBP) and classify the leukemic blast cells and normal cells. We have analysed the performance of machine learning and deep learning models such as Support Vector Machine (SVM), k-Nearest Neighbor algorithm (kNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). For classifying ALL and normal cell images, kNN achieved an accuracy of 94.4%, SVM, and ANN achieved an accuracy of 98.6%, and CNN achieved an accuracy of 99.6%. SVM achieved the highest sensitivity of 100%.