Abstract
Cerebrospinal fluid, Chest X-Ray, blood smear analysis, and bone marrow examination are several standards to uphold the diagnosis of some diseases. The most important how to uphold leukemia and malaria disease has counted the total numbers of white blood cells (WBC) under a microscope. The several procedures and standards of how to uphold the diseases were not comfortable to do caused a time-consuming and tedious process. Nowadays there are many pieces of research in recent years that have tried to solve the problem of how to uphold and diagnosing quickly the diseases. This research paper proposed a comparative study of convolutional neural network (CNN) and traditional image processing within cell imagery extraction. The types of diseases that we did in this research are Malaria cell classification (infected and uninfected) and Acute leukemia disease (ALL and AML). In case of the Malaria cells classification we got high accuracy about 95%, with simple four layers of the CNN. At the end of this research, we can conclude that a convolutional neural network (CNN) better a method for image classification in big data cases compared with traditional image processing. The accuracy of those experiments in CNN-Malaria has satisfactorily achieved accuracy above 95% while Acute leukemia-LVQ.1 got accuracy about 73% with traditional image processing.

Table of Contents
Ming Chi University of Technology Recommendation Letter from the Thesis Advisor………………………………………………………. i
Ming Chi University of Technology Thesis/Dissertation Oral Defense Committee Certification…………………………………………….. ii
Abstract iii
Table of Contents iv
List of Figures vii
List of Tables ix
Chapter 1 1
Introduction 1
1.1 Artificial Intelligence (AI) 1
1.2 Objectives 3
1.2.1 Objectives of CNN - Malaria 3
1.2.2 Objectives of Traditional Image Processing – Acute Leukemia 4
1.3 Outline 5
Chapter 2 6
Literature Review 6
2.1 Artificial Intelligence in Biomedical Engineering 6
2.2 Machine Learning (ML) 8
2.2.1 Data 9
2.2.2 Model 9
2.2.3 Learning 10
2.3 Deep Learning (DL) 11
2.3.1 Inside a Deep Neural Network 12
2.3.2 Deep Learning Algorithm 13
2.3.3 Artificial Intelligence - Machine Learning - Deep Learning 14
2.3.4 Convolutional Neural Network (CNN) 15
2.3.5 Parameters for Measurement of the Accuracy 22
2.4 Digital Image Processing 26
2.4.1 Data Acquisition 27
2.4.2 Image Pre-Processing 27
2.4.3 Feature Selection and Extraction 28
2.4.4 Color Feature Extraction 28
2.4.5 Texture Feature Extraction 30
2.5 Learning Vector Quantization (LVQ) 32
2.5.1 Algorithm of Learning Vector Quantization (LVQ 1) 33
2.6 Related Research Work in Malaria Disease 34
2.7 Related Research Work in Acute Leukemia Disease Case 35
Chapter 3 38
Experimental Procedure 38
3.1 Flowchart of the classification cell of malaria using CNN 38
3.1.1 Data Collecting and Data Modeling 39
3.1.2 Model Architecture of Convolutional Neural Network 42
3.2 Flowchart of the Acute Leukemia using LVQ.1 algorithm 45
3.2.1 Data Collecting 46
3.2.2 Data Acquisition 47
3.2.3 Data Division 47
3.2.4 Identification Process 48
Chapter 4 73
Result and Discussion 73
4.1 Malaria Disease Using CNN Algorithm 73
4.1.1 Classification Results Interface 74
4.1.2 Accuracy of The CNN’s Algorithm 76
4.1.3 Conclusion of Accuracy Testing Results 82
4.2 Acute Leukemia Disease Using Learning Vector Quantization.1 82
4.2.1 Classification Results Interface 82
4.2.2 Accuracy of The LVQ.1 Algorithm 86
4.2.3 Conclusions of Accuracy Testing Result 92
Chapter 5 93
Conclusion 93
5.1 Malaria Disease Using CNN Algorithm 93
5.2 Acute Leukemia Disease Using Learning Vector Quantization.1 94
5.3 Comparison Methods and Results 95
References 96

List of Figures
Figure 2.1 Simulation of AI in Medicine Fields 7
Figure 2.2 Comparison of Classic and Modern Programming 9
Figure 2.3 Architecture of Deep Learning algorithm in Case 12
Figure 2.4 Detail Architecture of Deep Learning algorithm in Case 12
Figure 2.5 Network Architecture of Deep Learning 13
Figure 2.6 How Deep Learning Works 14
Figure 2.7 Relations of AI, ML, and DL 15
Figure 2.8 Architecture of CNN (Feature Detection) 16
Figure 2.9 Architecture of CNN (Feature Detection / Feature Maps) 17
Figure 2.10 Example of a Convolutional Layer Operation 18
Figure 2.11 The Curve of ReLU Activation Function 19
Figure 2.12 Example of Pooling With A Stride and Filter Size of Two 19
Figure 2.13 Example of Two Fully-Connected Networks 20
Figure 2.14 Architecture of CNN (Classification) 21
Figure 2.15 Schema Classification of a Test Dataset Produces 22
Figure 2.16 Schema Calculating of Error Rate 23
Figure 2.17 Schema Calculating of Accuracy (ACC) 24
Figure 2.18 Schema Calculating of Sensitivity or Recall (REC) 24
Figure 2.19 Schema Calculating of Specificity 25
Figure 2.20 Schema Calculating of Precision 25
Figure 2.21 Corresponding of Distance and Angel of Matrix 3×3 31
Figure 2.22 Network Structure of LVQ.1 32
Figure 2.23 Samples Single-Cell of Malaria Disease 41
Figure 2.24 Architecture of Convolutional Neural Network (CNN) 42
Figure 4.1 Interface of Commond-promt Running Program .……… 74
Figure 4.2 Interface Directory File of The Program …………….…. 75
Figure 4.3 Interface of The Cell Image ……………………….…… 76
Figure 4.4 Interface of The Curve of Training Program ……….….. 76
Figure 4.5 Interface of The Image Database 83
Figure 4.6 Interface While Before Training Data 84
Figure 4.7 Interface Doing Learning Process 85
Figure 4.8 Interface Final Result of The Classification 85

List of Tables
Table 3.1 Data modeling For Training and Testing 42
Table 3.2 Network Details 43
Table 3.5 Flowchart Research of Acute Leukemia – LVQ.1 45
Table 3.4 Matrix Work Area 1 58
Table 3.5 Matrix co-occurrence with angle 00 59
Table 3.6 Matrix co-occurrence with angle 450 60
Table 3.7 Matrix co-occurrence with angle 900 62
Table 3.8 Matrix co-occurrence with angle 1350 63
Table 3.9 Values of Spatial Relationship with angle

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