A Computer Vision-Based method for breast cancer histopathological image classification by deep learning approach

BỘ GIÁO DỤC VÀ ĐÀO TẠO

TRƯỜNG ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINH

BÙI HUỲNH THÚY MAI

A COMPUTER VISION-BASED METHOD FOR BREAST CANCER

HISTOPATHOLOGICAL IMAGE CLASSIFICATION

BY DEEP LEARNING APPROACH

LUẬN VĂN THẠC SĨ KHOA HỌC MÁY TÍNH

TP. HỒ CHÍ MINH THÁNG 02 NĂM 2020

BỘ GIÁO DỤC VÀ ĐÀO TẠO

TRƯỜNG ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINH

BÙI HUỲNH THÚY MAI

A COMPUTER VISION-BASED METHOD FOR BREAST CANCER

HISTOPATHOLOGICAL IMAGE CLASSIFICATION

BY DEEP LEARNING APPROACH

Chuyên ngành : Khoa Học Máy Tính

Mã số chuyên ngành : 60 48 01 01

LUẬN VĂN THẠC SĨ KHOA HỌC MÁY TÍNH

Người hướng dẫn khoa học:

TS. TRƯƠNG HOÀNG VINH

TP. HỒ CHÍ MINH THÁNG 02 NĂM 2020

Contents

Acknowledgment ii

Abstract iii

Notations iv

Abbreviations v

1 Literature review of breast cancer histopathological image classification 1

1.1 Introduction and general considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Goals of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Contribution of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Foundational theory 12

2.1 Deep neuron network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.1.1 Introduction to deep neuron network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.1.2 Present the techniques of neuron network training . . . . . . . . . . . . . . . . . . . . 20

2.1.3 Present the popular deep network models . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2 Generative Adversarial Networks (GAN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.2.1 Introduction to GAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.2.2 Present the techniques of GAN training . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2.3 Present the popular GAN models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2

3 Experiment and Discussion 42

3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.2.1 BreaKHis dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.2.2 BACH dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.2.3 IDC dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.3 Experimental result on three datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.4 Comparing to handcrafted features and deep features for classification . . . . . . . . . . . . . 50

4 Conclusion 53

List of Tables 55

List of Figures 55

Bibliography 57

i

Acknowledgment

I sincerely thank my advisor, Dr. Vinh Truong Hoang - Ho Chi Minh City Open University, for guiding me

to complete the thesis.

ii

Abstract

Computer vision field has became more active in the recent decades when scientists found to apply math￾ematical and quantitative analysis. Various applications have been using computer vision techniques to

improve their productivity such as visual surveillance, robotic, autonomous vehicle, and specially medical

image processing. Until Geoffrey Hinton and Yann LeCun, both known as “Godfather of deep learning”

used Neural Networks and Back Propagation in characters and handwritten prediction given the best result

comparing to previous works, the techniques has been became prominent.

In this thesis, we focus to detect the breast cancer with high accuracy in order to decrease the examination

cost in accepted time. So, we choose the deep learning to research and evaluate our approach on three datasets

such as BreaKHis, BACH and IDC. Due to some limitations of deep learning and dataset sizes, we propose

the composition of popular techniques to be boosting the efficient classification, they are transfer learning,

Generative Adversarial Network (GAN) and neural networks. VGG16 & VGG19 are the base models which

are applied to extract the high level features space from patch cropped images, naming as multi deep features

before being trained by neuron nets. So far, there are not any works to leverage GAN power to generate

the fake BreaKHis and in our thesis, we use Pix2Pix and StyleGAN model as generator model. With the

proposed approach, the cancer detection results achieve the better performance to some existing works with

98% in accuracy for BreaKHis, 96% for BACH and 86% for IDC.

iii

Notations

l Number of block of layers are stacked together

Φ(x) The hypothesis space in traditional machine learning

L(Φ(x)) Loss function for each hypothesis

σ Activation function in deep learning

f , g Mapping function in deep learning

x Input feature

w Feature’s weight

y Output feature

θ Loss function in GAN model

D(x) Discriminator model

G(x) Generator model

z Noise input

E Mean

Var Variance

iv

Abbreviations

LBP Local Binary Pattern

WHO World Health Organization

GLOBOCAN Global Cancer Incidence, Mortality and Prevalence

CBE Clinical Breast Exam

CLBP Completed Local Binary Pattern

LPQ Local Phase Quantization

GLCM Gray Level Co-Occurrence Matrices

PFTAS Free Threshold Adjacency Statistic

ORB Oriented FAST and Rotated BRIEF

k-NN k-Nearest Neighbor

SVM Support Vector Machines

RF Random Forest

QDA Quadratic discriminant analysis

GPU Graphic Processing Unit

CNN Convolution neuron network

CONV Convolutional layer

FC Fully connected layer

MAE Manifold Persevering Autoencoder

v