人体CT影像肺癌的识别-基于海思.zip

import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator # 数据预处理 train_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory('train', target_size=(150, 150), batch_size=32) # 构建模型 model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) # 模型训练 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(train_generator, epochs=10) # 肺癌识别 test_img = tf.keras.preprocessing.image.load_img('test.png', target_size=(150, 150)) test_img_array = tf.keras.preprocessing.image.img_to_array(test_img) test_img_array = np.expand_dims(test_img_array, axis=0) prediction = model.predict(test_img_array) if prediction[0][0] > 0.5: print('肺癌可能性较大') else: print('肺癌可能性较小')