Deep Learning for Identifying Breast Cancer Using GoogLeNet

目录

声明

Acknowledgements

Abstract

Table of contents

List of figures

List of tables

Chapter 1 Introduction

1．1 Research background and significance

1．2 Breast cancer imaging diagnostic technology

1．3 Computer-Aided Diagnosis Technology

1．4 Main content and arrangement of this paper

1．4．1 Main research contents of this paper

1．4．2 Main Arrangements of the Paper

Chapter 2 Medical Image Processing and Deep Learning Foundation

2．1 Image data processing

2．1．1 Image multi-resolution expression

2．1．2 Color Image Processing

2．1．3 Image segmentation

2．1．4 Data Augmentation

2．2 Deep learning foundation

2．2．1 Learning from shallow to deep learning

2．2．2 Unsupervised deep learning algorithm

2．2．3 Supervised deep learning algorithm

2．3 Convolutional Neural Network Theory

2．3．1 Convolutional Neural Networks

2．3．2 GoogLeNet Convolutional Neural Network

2．3．3 Deep residual convolutional neural network

2．4 Summary of this chapter

Chapter 3 Medical Image Recognition Based on GoogLeNet

3．1 Introduction

3．2 Breast cancer pathology image classification process

3．2．1 Setting up an experimental environment

3．2．2 Build data set

3．2．3 Image preprocessing

3．3 GoogLeNet neural network model

3．3．1 GoogLeNet neural network model construction

3．3．2 Softmax classifier

3．3．3 Model training

3．4 Experimental results and analysis

3．4．1 Evaluation standard

3．4．2 Result analysis

3．4．3 Experimental summary

3．5 Summary of this chapter

Chapter 4 Breast cancer classification system based on GoogLeNet transfer model

4．1 Introduction

4．2 Build data set

4．2．1 Data preparation

4．2．2 Data augmentation

4．3 Transfer learning

4．4 Breast cancer classification and recognition system

4．4．1 Setup model

4．4．2 Training strategy

4．5 Result analysis

4．6 Summary of this chapter

Chapter 5 Summary and Outlook

5．1 Summary

5．2 Future research plan

References

Appendix