Date of Award
2019
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Program
Software Engineering
Department
Computer Science
College
Engineering
Faculty Advisor
Ludwig, Simone
Description
Detection of ALL can be done through the analysis of white blood cells (WBCs) called leukocytes. Usually, the analysis of blood cells is performed manually by skilled operators, have numerous drawbacks, such as slow analysis, a non-standard accuracy and skill of the operator. Hence many automated systems are using in order to analyze and classify the blood cells. This paper focuses on an automatic system based on image processing algorithms for the classification of blood cells for detection of Acute Lymphocytic Leukemia (ALL). Experiments were ran using 20 models with PCA and seven models namely Medium KNN, Coarse KNN, Cosine KNN, Cubic KNN, Weighted KNN, Ensemble Boosted trees and Ensemble Bagged trees had 99.9% accuracy. These models are evaluated based on the prediction speed, training time, confusion matrix and ROC. Of all models, the weighted KNN classifier is best when using PCA.
Recommended Citation
Arya, Minakshi, "Automated Detection of Acute Leukemia Using K-Means Clustering Algorithm" (2019). Computer Science. 339.
https://digitalcommons.ndsu.edu/computer-sci/339
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/29846