基于卷积神经网络的CT图像目标检测代码源码

以下是一个基于卷积神经网络的CT图像目标检测代码示例，使用的是Python语言和Keras深度学习框架。

# 导入必要的库
import numpy as np
import keras
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, Flatten, Dense

# 构建卷积神经网络模型
input_layer = Input(shape=(512, 512, 1))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(input_layer)
pool1 = MaxPooling2D((2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
pool2 = MaxPooling2D((2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
pool3 = MaxPooling2D((2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
pool4 = MaxPooling2D((2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
dropout1 = Dropout(0.5)(conv5)
flatten = Flatten()(dropout1)
dense1 = Dense(256, activation='relu')(flatten)
dropout2 = Dropout(0.5)(dense1)
output_layer = Dense(1, activation='sigmoid')(dropout2)

model = Model(inputs=[input_layer], outputs=[output_layer])

# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 加载数据集并进行训练
X_train = np.load('X_train.npy')
y_train = np.load('y_train.npy')
model.fit(X_train, y_train, epochs=10, batch_size=32)

# 保存模型
model.save('ct_detection_model.h5')

注：此处代码仅为示例，实际使用时需要根据具体的数据集和任务进行修改。