Using convolutional neural networks for fine grained image classification of acute lymphoblastic leukemia

<p>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.</p>

Name thesis:degree_name

Master of Science (MS) in Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2016

Author dc:creator

• Sipes, Richard K.

• Bioelectrical and Neuroengineering
• Diagnosis

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/407

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1407