Using Convolutional Neural Networks for Automated Fine Grained Image Classification of Acute Lymphoblastic Leukemia

Abstract

Acute lymphoblastic leukemia can be diagnosed through a series of tests which include the minimally invasive microscopic examination of a stained peripheral blood smear. Manual microscopy is a slow process with variable accuracy depending on the laboratorian's skill level. Thus automating microscopy is a goal in cell biology. Current methods involve hand-selecting features from cell images as inputs to a variety of standard machine learning classifiers. Underrepresented in this filed, yet successful in practice, is the convolutional neural network that learns features from fine-grained images. This paper compares the performance of a convolutional neural network model with other models to determine the validity of using whole cell images rather than hand-selected features for acute lymphoblastic leukemia classification.