-
Notifications
You must be signed in to change notification settings - Fork 0
/
brain_tumor_v1.py
85 lines (65 loc) · 3.03 KB
/
brain_tumor_v1.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
import os
import numpy as np
images = np.load("BD_Img.npy")
labels = np.load("BD_Label.npy")
from keras.models import Model
from keras.layers import Dense, Flatten, Input, Conv2D, MaxPooling2D, Dropout, concatenate, Conv2DTranspose, UpSampling2D, Activation
# Define the U-Net model architecture
def unet_model(input_shape):
# Input layer
inputs = Input(input_shape)
# Contracting path
conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
# Skipping connection
conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
# Expanding path
conv4 = Conv2D(512, 3, activation='relu', padding='same')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same')(conv4)
# Upsampling and concatenation
up5 = Conv2DTranspose(256, 2, strides=(2, 2), padding='same')(conv4)
up5 = concatenate([up5, conv3], axis=3)
conv5 = Conv2D(256, 3, activation='relu', padding='same')(up5)
conv5 = Conv2D(256, 3, activation='relu', padding='same')(conv5)
up6 = Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(conv5)
up6 = concatenate([up6, conv2], axis=3)
conv6 = Conv2D(128, 3, activation='relu', padding='same')(up6)
conv6 = Conv2D(128, 3, activation='relu', padding='same')(conv6)
up7 = Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(conv6)
up7 = concatenate([up7, conv1], axis=3)
conv7 = Conv2D(64, 3, activation='relu', padding='same')(up7)
conv7 = Conv2D(64, 3, activation='relu', padding='same')(conv7)
flatten = Flatten()(conv7)
# Output layer for binary classification
output = Dense(2,activation='softmax')(flatten)
model = Model(inputs=inputs, outputs=output)
return model
# Set the input shape for the U-Net model
input_shape = (256, 256, 1) # Assuming grayscale CT scan images
# Create the U-Net model for binary classification
model = unet_model(input_shape)
# Print the summary of the U-Net model
model.summary()
import numpy as np
data_x = np.load("BD_Img.npy")
data_y = np.load("BD_Label.npy")
data_y.shape
data_y_encod = np.zeros((3000,2))
for i,y in enumerate(data_y):
data_y_encod[i][y]=1
data_y_encod.shape
data_y_encod[0]
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(data_x,data_y_encod,test_size=0.2)
from keras.optimizers import Adam
from keras.losses import CategoricalCrossentropy
model.compile(optimizer=Adam(learning_rate=1e-4),loss=CategoricalCrossentropy(),metrics=['accuracy'])
model.fit(x_train,y_train,batch_size=32,epochs=1,validation_split=0.2)
model.evaluate(x_test,y_test)
model.save("brain_tumor_v1")