opensourcefinal

• This program is for detecting and distinguishing brain tumors by MRI pircture.

Load Pakages

import os

import sklearn.datasets import sklearn.linear_model import sklearn.svm import sklearn.tree import sklearn.ensemble import sklearn.model_selection import sklearn.metrics

import skimage.io import skimage.transform import skimage.color

import numpy as np

import matplotlib.pyplot as plt %matplotlib inline

from sklearn.ensemble import VotingClassifier, ExtraTreesClassifier import matplotlib.pyplot as plt

• Pakages that I imported

Load Data

image_size = 64 labels = ['glioma_tumor','meningioma_tumor','no_tumor','pituitary_tumor']

images = [] y = [] for i in labels: folderPath = os.path.join('./tumor_dataset/Training',i) for j in os.listdir(folderPath): img = skimage.io.imread(os.path.join(folderPath,j),) img = skimage.transform.resize(img,(image_size,image_size)) img = skimage.color.rgb2gray(img) images.append(img) y.append(i)

images = np.array(images)

X = images.reshape((-1, image_size**2)) y = np.array(y)

X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.3, random_state=0)

Since there is additional test dataset, I used all the data on trainning

X_train = X y_train = y

Model

y_pred = np.zeros_like(y_test)

ETC = ExtraTreesClassifier(n_estimators=271, random_state = 1000) ETC.fit(X_train, y_train) y_pred = ETC.predict(X_test)

print('Accuracy: %.2f' % sklearn.metrics.accuracy_score(y_test, y_pred))

• Please commit the code in order
• licence: mit licence #contact information
• william.joo03@gmail.com