用卷积神经网络识别肺部ct的png格式图片代码
时间: 2023-06-25 14:06:22 浏览: 90
以下是一个使用卷积神经网络(CNN)识别肺部CT图片的代码示例:
首先,导入必要的库和模块:
```python
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers
from tensorflow.keras import models
```
接下来,设置训练和验证数据的目录和参数:
```python
train_dir = 'path/to/train/directory'
validation_dir = 'path/to/validation/directory'
# 训练数据增强器,用于随机改变图像以增加数据多样性
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# 验证数据不需要增强
validation_datagen = ImageDataGenerator(rescale=1./255)
# 将图像输入分批次读入内存
train_batchsize = 32
val_batchsize = 10
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=train_batchsize,
class_mode='binary')
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=val_batchsize,
class_mode='binary')
```
然后,定义一个简单的CNN模型:
```python
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
```
编译模型并开始训练:
```python
model.compile(loss='binary_crossentropy',
optimizer=tf.keras.optimizers.RMSprop(lr=1e-4),
metrics=['accuracy'])
history = model.fit(
train_generator,
steps_per_epoch=train_generator.samples/train_generator.batch_size ,
epochs=30,
validation_data=validation_generator,
validation_steps=validation_generator.samples/validation_generator.batch_size,
verbose=2)
```
最后,使用训练好的模型对新的CT图像进行预测:
```python
from tensorflow.keras.preprocessing import image
def predict_image(filename):
img = image.load_img(filename, target_size=(150, 150))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict(images, batch_size=10)
return classes[0]
```
这就是使用CNN识别肺部CT图像的代码示例。
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