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Alexnet_all_datas.py
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Alexnet_all_datas.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import SimpleITK as sitk
import os
import numpy as np
# import matplotlib.pyplot as plt
import nibabel as nib
from skimage.transform import resize
from skimage import io
from sklearn.utils import shuffle
# In[2]:
import keras
from keras.models import Sequential
from keras.layers import Dense,Activation,Dropout,Flatten,Conv2D,MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, Callback
# In[4]:
def find_images(path_dir, path_nor, path_t):
num=50
X_train=np.zeros(((274+109)*num+842,240,240),dtype=np.uint16)
Y_train=np.zeros(((274+109)*num+842,2),dtype=np.uint16)
j=0
for item in os.listdir(path_dir):
item=os.path.join(path_dir,item)
for item2 in os.listdir(item):
im ={'T1':None,'gt':None}
item2=os.path.join(item,item2)
for item3 in os.listdir(item2):
item3=os.path.join(item2,item3)
for item4 in os.listdir(item3):
item5=os.path.join(item3,item4)
if os.path.isfile(item5) and item5.endswith('.mha'):
itk_image = sitk.ReadImage(item5)
nd_image = sitk.GetArrayFromImage(itk_image)
if 'more' in item5 or 'OT' in item5:
im['gt']=nd_image
elif 'T1' in item5 and 'T1c' not in item5:
im['T1']=nd_image
for i in range(70,70+num):
if not sum(sum(im['gt'][i,:,:])): #sum=0 normal brains
Y_train[j][0]=1
else:
Y_train[j][1]=1
X_train[j]=im['T1'][i,:,:]
j+=1
for item in os.listdir(path_nor):
item2 = os.path.join(path_nor,item)
img = nib.load(item2)
data=img.get_fdata()
for i in range(90,90+num):
Y_train[j][0]=1
X_train[j]=resize(data[50:210,i,:],(240,240))
j+=1
for item in os.listdir(path_t):
item2=os.path.join(path_t,item)
for item3 in os.listdir(item2):
item5=os.path.join(item2,item3)
if os.path.isfile(item5) and item5.endswith('.jpg'):
image = io.imread(item5,as_gray=True)
image = resize(image,(240,240))
X_train[j]=image
if item == 'abnormalsJPG':
Y_train[j][1]=1
elif item == 'normalsJPG':
Y_train[j][0]=1
j+=1
return X_train,Y_train
# In[5]:
path_dir="../BRATS2015_Training"
path_nor="../nii"
path_t="../normalsVsAbnormalsV1"
X_train,Y_train=find_images(path_dir, path_nor, path_t)
# In[15]:
X_train=X_train-np.mean(X_train,axis=0)
np.save("mean",np.mean(X_train,axis=0))
shape=X_train.shape
X_train=X_train.reshape(shape[0],shape[1],shape[2],1)
# In[16]:
np.random.seed(1000)
model=Sequential()
model.add(Conv2D(filters=96, input_shape=(240,240,1), kernel_size=(11,11), strides=(4,4), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid'))
model.add(BatchNormalization())
model.add(Conv2D(filters=256,kernel_size=(11,11),strides=(1,1),padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid'))
model.add(BatchNormalization())
model.add(Conv2D(filters=384,kernel_size=(3,3),strides=(1,1),padding='valid'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=384,kernel_size=(3,3),strides=(1,1),padding='valid'))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=256,kernel_size=(3,3),strides=(1,1),padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(2))
model.add(Activation('sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
# In[18]:
X_train, Y_train = shuffle(X_train, Y_train)
model_checkpoint = ModelCheckpoint('./Alexnet_brat.hdf5', monitor='loss',verbose=1, save_best_only=True)
reduce_lr = ReduceLROnPlateau(factor=0.5, patience=3, min_lr=0.000001, verbose=1)
callbacks = [reduce_lr, model_checkpoint]
#model.load_weights("./Alexnet_brat.hdf5")
model.fit(X_train,Y_train,batch_size=32,epochs=200,verbose=1,validation_split=0.2,shuffle=True, callbacks=callbacks)
# In[12]:
#plt.imshow(X_train[13699])
# In[14]:
# Y_train[13699]