Date of Award
Fall 2016
Rights
Access is available to all users
Document Type
Thesis
Degree Name
Master of Science (MS) in Computer Science
Department
Computer Science
Abstract
Acute lymphoblastic leukemia (ALL) is a cancer of bone marrow stems cells that results in the overproduction of lymphoblasts. ALL is diagnosed through a series of tests which includes the minimally invasive microscopic examination of a stained peripheral blood smear. During examination, lymphocytes and other white blood cells (WBCs) are distinguished from abnormal lymphoblasts through fine-grained distinctions in morphology. Manual microscopy is a slow process with variable accuracy that depends on the laboratorian's skill level. Thus automating microscopy is a goal in cell biology. Current methods involve hand-selecting features from cell images for input to a variety of standard machine learning classfiers. Underrepresented in WBC classification, yet successful in practice, is the convolutional neural network (CNN) that learns features from whole image input. Recently, CNNs are contending with humans in large scale and fine-grained image classification of common objects. In light of their effectiveness, CNNs should be a consideration in cell biology. This work compares the performance of a CNN with standard classifiers to determine the validity of using whole cell images rather than hand-selected features for ALL classification.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Sipes, Richard K., "Using convolutional neural networks for fine grained image classification of acute lymphoblastic leukemia" (2016). EWU Masters Thesis Collection. 407.
https://dc.ewu.edu/theses/407
