Acute Leukemia Classification Using Sequential Neural Network Classifier in Clinical Decision Support System
- Alternative Title
- 임상적 결정 지원 시스템에서 순차 신경망 분류기를 이용한 급성 백혈병 분류 기법
- Abstract
- Blood, which has a very important role for human body transportation system, is mainly consist of three main elements: Red Blood Cells (RBCs), White Blood Cells (WBCs), and Platelets. Particular diseases could possibly attack these blood elements and leukemia or white blood cancer has been considered as one of the most fatal diseases that compromise white blood cells functions as the main immunity preservation agent. The numbers of new leukemia cases has been increased within these past years, while the survivability rate has not shown any improvement. The author suspect that one of the reasons is due to speed inadequacy in leukemia type classification and diagnostic process. In fact, in many cases rapid identification of the leukemia type are still troublesome, thus a simple, rapid, and reliable clinical decision support system is always become a necessity to support clinician diagnosis performance. According to Frecnch-American-British classification, there are four types of leukemia disease which is classified based on their severity level and infected cells type, they are Acute Myeloid Leukemia (AML), Acute Lymphoid Leukemia (ALL), Chronic Myeloid Leukemia (CML), and Chronic Lymphoid Leukemia (CLL). In this paper, the author would like to propose a novel approach to perform only acute leukemia type classification utilizing effective classifier architecture. The proposed method includes several conventional image pre-processing, image clustering, and image segmentation method to extract region of interest, along with feature extraction analysis using PCA method, and finally followed by neural network classifier which provide an excellent performance in classification process that reach 97.95% of accuracy to discriminate normal and cancerous cell images from given database
- Issued Date
- 2015
- Awarded Date
- 2015. 2
- Type
- Dissertation
- Publisher
- Pukyong National University, Department of IT Convergence and Application Engineering
- Affiliation
- Pukyong National University, Department of IT Convergence and Application Engineering
- Department
- 대학원 IT융합응용공학과
- Advisor
- Ki-Ryong Kwon
- Table Of Contents
- Ⅰ. Introduction 1
Ⅱ. Related Works 5
2.1 Overview of Leukemia 5
2.1.1 Acute Lymphocytic Leukemia (ALL) 5
2.1.2 Acute Myeloid Leukemia (AML) 6
2.1.3 Chronic Lymphocytic Leukemia (CLL) 8
2.1.4 Chronic Myeloid Leukemia (CML) 9
2.2 Conventional Leukemia Detection and Classification Methods 10
2.3 Conventional Clinical Decision Support System Technique for Leukemia Diagnostic 13
Ⅲ. Proposed Classification Methodology 15
3.1 Frameworks and Constrains 15
3.2 Image Pre-processing, Clustering, and Segmentation 16
3.2.1 RGB to CIE L*a*b color conversion 17
3.2.2 Three Classes K-Means Clustering 18
3.2.3 OTSU Thresholding, Morphological Filter, Area Opening, and Mask Building 20
3.3 Grouped Cells Separation Procedure 21
3.4 Feature Selection and Extraction 21
3.4.1 Feature Selection for Normal and Abnormal Cell Images 22
3.4.2 Feature selection for Lymphoctyic and Myeloid leukemia 25
3.5 Sequential Neural Network Classifier 27
3.5.1 Neural Network Classifier Concatenation 28
3.5.2 First Neural Network Classifier 28
3.5.3 Second Neural Network Classifier 30
Ⅳ. Experimental Result and Analysis 33
4.1 Leukemia Image Database 33
4.2 First Feature Extraction result 34
4.3 Second Feature Extraction Result 35
4.4 Classification Result 37
Ⅴ. Conclusion and Future Works 39
References 40
Acknowledgements 45
- Degree
- Master
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