Báo cáo ( Bằng tiếng anh) Xử lý ảnh y tế (não người) Medical imaging

AI for Medical imaging Name Course Master Student ID Dataset BrainTumor 1 Introduction The main task of this project Binary Classification Predict the MGMT methylation status using MRI from patients with brain tumor 1 1 What is the MGMT? Glioblastoma is the most frequent malignant primary tumor in the brain It has a very poor prognosis, with a median survival of less than a year The current standard if care consists of surgical resection followed by radiotherapy in addition to alkylating chemoth.

AI for Medical imaging

1 Introduction

The main task of this project: Binary Classification

Predict the MGMT methylation status using MRI from patients with brain tumor

1.1 What is the MGMT?

Glioblastoma is the most frequent malignant primary tumor in the brain It has a very poor prognosis, with a median survival of less than a year The current standard if care consists of surgical resection followed by radiotherapy in

addition to alkylating chemotherapy with temozolomide

MGMT (O[6]-methylguanine – DNA methyltransferase) is a DNA repair

enzyme This enzyme rescues tumor cells from alkylating agent-induced

damage, leading to chemotherapy resistance with alkylating agents

1.2 MRI and MGMT Connection

MGMT promotor methylated glioblastoma is likely to show less aggressive imaging feature than MGMT promotor unmethylated glioblastoma

2 Datasets

Dataset link: https://1drv.ms/u/s!AsG5zlY5lnaKtMdjgnkybLzavG19iw?

e=BPWmBu

Data Description

Patients in training sets 400 Patients in testing sets 185

There are 4 sub-folders, each of them corresponding to each of the MRI scans,

in DICOM format, included:

+ Fluid Attenuated Inversion Recovery (Flair)

+ T1 – weighted pre – contrast (T1w)

+ T2 – weighted contrast enhanced (T1CE)

+ T2 – weighted (T2)

The dataset structure:

Train/Test/Validation

| _00000

| | _FLAIR

| | |Image-1.dcm

| | |Image-2.dcm

| | | …

| | _T1w

| | |Image-1.dcm

| | |Image-2.dcm

| | |…

| | _T1wCE

| | |Image-1.dcm

| | |Image-2.dcm

| | |…

| | _T2w

| | |Image-1.dcm

| | |Image-2.dcm

| | |…

train/ folder: contain the training files

labels.csv: contain the target MGMT_value for each subject in the training data test/ folder: contain the testing files

Figure 1 : The bar graph for labels.csv file

Figure 2: The bar graph for train data

Figure 3: The pie chart for labels.csv

In this project, the sub-folders FLAIR and T1wCE were used

3 Method

The workflow:

Support Vector Machines Result Input image

(Dcm file) Convert to gray

Support-vector machines (SVMs) are supervised learning models with

associated learning algorithms that analyze data for classification and regression analysis

SVC is a similar method that also builds on kernel functions but is appropriate for unsupervised learning

I use class sklearn.svm.SVC

4 Results

For training:

The mean accuracy:

For validation:

For testing:

The probability of each patient was saved in submission_c1.csv

Classification result for test data

5 Conclusion

+ The result was generate and it is not good

+ In the future, I need to apply the deep learning method in this problem to improve the accuracy

+ Limitation: The time of semester is limited

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