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3DCNN.py
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3DCNN.py
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# coding: utf-8
# In[2]:
import os
import glob
import os
import nibabel as nib
import numpy as np
from matplotlib import pyplot as plt
os.environ["CUDA_VISIBLE_DEVICES"]="1"
# In[3]:
from keras.models import Sequential
from keras.layers import Conv3D, Activation, MaxPooling3D, BatchNormalization, Flatten, Dropout, Dense, GaussianNoise
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.losses import binary_crossentropy
from sklearn.model_selection import train_test_split
from keras.preprocessing.image import ImageDataGenerator as IDG
# In[4]:
def Get_data(paths):
hdr = []
data = []
label = []
for path in paths:
if os.path.isdir(path) is True:
pathnow = os.path.join(path)
data_path = glob.glob(pathnow + '/*seg.nii.gz')
nibfile = nib.load(data_path[0])
data.append(nibfile.get_data())
hdr.append(nibfile.header)
return np.array(data), hdr
# In[5]:
paths = glob.glob('/data/BraTS/BraTS17/MICCAI_BraTS17_Data_Training/HGG/*')
data_HGG, hdr_HGG = Get_data(paths)
paths = glob.glob('/data/BraTS/BraTS17/MICCAI_BraTS17_Data_Training/LGG/*')
data_LGG, hdr_LGG = Get_data(paths)
label = np.zeros((285))
label[0:len(data_HGG)] = 1
label[len(data_HGG):len(data_HGG) + len(data_LGG)] = 0
# In[6]:
data = np.concatenate((data_HGG, data_LGG))/4.0
# In[21]:
data[0].shape
# In[7]:
# def Segmet(img, up, down, axis):
# for i in range(up):
# img = np.delete(img, 90 - up, axis)
# for i in range(down):
# img = np.delete(img, 0, axis)
# return img
# In[8]:
# data = Segmet(data, 20, 20, 1)
# data = Segmet(data, 20, 20, 2)
# data = Segmet(data, 20, 20, 3)
# In[35]:
data.shape
# In[36]:
# data_train = data.reshape((285,200,200,115,1))
# In[7]:
data_train = data.reshape((285,240,240,155,1))
# In[8]:
X_train, X_test, y_train, y_test = train_test_split(data_train, label, test_size = 0.2)
# In[38]:
X_train.shape[1:]
# In[12]:
def plot_image(img, x, y, z):
plt.subplot(131)
plt.imshow(img[::,::,z])
plt.subplot(132)
plt.imshow(img[::,y,::])
plt.subplot(133)
plt.imshow(img[x,::,::])
# In[18]:
#plot_image(data[5], 120, 120, 70)
# In[18]:
def model():
model = Sequential()
model.add(Conv3D(8, kernel_size=(2,2,2), input_shape=(X_train.shape[1:]), activation='relu'))
model.add(MaxPooling3D(pool_size=([2,2,2])))
model.add(Conv3D(32, kernel_size=(2,2,2), activation='relu'))
model.add(MaxPooling3D(pool_size=([2,2,2])))
model.add(Conv3D(64, kernel_size=(2,2,2), activation='relu'))
model.add(MaxPooling3D(pool_size=([2,2,2])))
model.add(Conv3D(128, kernel_size=([2,2,2]), activation='relu'))
model.add(MaxPooling3D(pool_size=([3,3,3])))
model.add(Conv3D(256, kernel_size=(2,2,2), activation='relu'))
model.add(MaxPooling3D(pool_size=([3,3,3])))
model.add(GaussianNoise(0.001))
model.add(Flatten())
#model.add(BatchNormalization())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
return model
# In[19]:
model = model()
# In[26]:
model.summary()
# In[32]:
hist = model.fit(X_train,
y_train,
validation_split=0.2,
batch_size = 3,
epochs = 10,
#callbacks = [early_stopping],
shuffle=True)
# In[15]:
def save_history(history, result_dir):
loss = history.history['loss']
acc = history.history['acc']
val_loss = history.history['val_loss']
val_acc = history.history['val_acc']
nb_epoch = len(acc)
with open(os.path.join(result_dir, 'result.txt'), 'w') as fp:
fp.write('epoch\tloss\tacc\tval_loss\tval_acc\n')
for i in range(nb_epoch):
fp.write('{}\t{}\t{}\t{}\t{}\n'.format(
i, loss[i], acc[i], val_loss[i], val_acc[i]))
# In[24]:
def plot_history(history):
plt.plot(history.history['acc'], marker='.')
plt.plot(history.history['val_acc'], marker='.')
plt.title('model accuracy')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.grid()
plt.legend(['acc', 'val_acc'], loc='lower right')
plt.savefig('./../result/model_accuracy.png')
plt.close()
plt.plot(history.history['loss'], marker='.')
plt.plot(history.history['val_loss'], marker='.')
plt.title('model loss')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.legend(['loss', 'val_loss'], loc='upper right')
plt.savefig('./../result/model_loss.png')
plt.close()
# In[25]:
plot_history(hist)
# In[78]:
def plot_now(history):
plt.subplot(121)
plt.plot(history.history['acc'], marker='.')
plt.plot(history.history['val_acc'], marker='.')
plt.title('model accuracy')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.grid()
plt.legend(['acc', 'val_acc'], loc='lower right')
plt.subplot(122)
plt.plot(history.history['loss'], marker='.')
plt.plot(history.history['val_loss'], marker='.')
plt.title('model loss')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.legend(['loss', 'val_loss'], loc='upper right')
# In[79]:
plot_now(hist)
# In[81]:
loss, acc = model.evaluate(X_test, y_test, verbose=0)
# In[60]:
import pandas as pd
# In[74]:
test_pred = X_test[:17]
test_pred = model.predict_classes(test_pred)
test_true = y_test[:17]
test_true = test_true.astype(int)
test_pred = test_pred.squeeze()
pd.crosstab(test_true, test_pred, rownames=['label'], colnames=['predict'])
# In[75]:
test_pred = X_test[17:34]
test_pred = model.predict_classes(test_pred)
test_true = y_test[17:34]
test_true = test_true.astype(int)
test_pred = test_pred.squeeze()
pd.crosstab(test_true, test_pred, rownames=['label'], colnames=['predict'])
# In[76]:
test_pred = X_test[34:51]
test_pred = model.predict_classes(test_pred)
test_true = y_test[34:51]
test_true = test_true.astype(int)
test_pred = test_pred.squeeze()
pd.crosstab(test_true, test_pred, rownames=['label'], colnames=['predict'])
# In[77]:
test_pred = X_test[51:57]
test_pred = model.predict_classes(test_pred)
test_true = y_test[51:57]
test_true = test_true.astype(int)
test_pred = test_pred.squeeze()
pd.crosstab(test_true, test_pred, rownames=['label'], colnames=['predict'])
# In[65]:
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