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我正在尝试将脑肿瘤的 MRI 图像分类为正常、恶性或良性。为此，我想在一个程序中运行两个神经网络。第一个网络对大脑 MRI 图像是肿瘤还是非肿瘤进行分类。如果是肿瘤，第二个网络对脑MRI图像是恶性还是良性进行分类。代​​码如下： 

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
import sys
from PIL import Image
sys.modules['Image'] = Image 
#import PIL as pillow
#from PIL import Image
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (100, 100, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('C:\\Users\\Admin\\Desktop\\tumor_non_tumour\\training',
target_size = (100, 100),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('C:\\Users\\Admin\\Desktop\\tumor_non_tumour\\testing',
target_size = (100, 100),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 10,
epochs = 20,
validation_data = test_set,
validation_steps = 10)
# Part 3 - Making new predictions
import numpy as np
from tkinter import *
from tkinter import filedialog
root  = Tk()
from keras.preprocessing import image
test_image = image.load_img(filedialog.askopenfilename( filetypes = ( ("image files" , "*.jpg") , ("all files", "*.*"))), target_size = (100, 100))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] == 0:
    prediction = 'normal'    
else :
    prediction = 'tumorous'

#print(prediction)

if(result[0][0] == 1) :
    sys.modules['Image'] = Image 
    #import PIL as pillow
    #from PIL import Image
    # Initialising the CNN
    classifier = Sequential()
    # Step 1 - Convolution
    classifier.add(Conv2D(32, (3, 3), input_shape = (100, 100, 3), activation = 'relu'))
    # Step 2 - Pooling
    classifier.add(MaxPooling2D(pool_size = (2, 2)))
    # Adding a second convolutional layer
    classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
    classifier.add(MaxPooling2D(pool_size = (2, 2)))
    # Step 3 - Flattening
    classifier.add(Flatten())
    # Step 4 - Full connection
    classifier.add(Dense(units = 128, activation = 'relu'))
    classifier.add(Dense(units = 1, activation = 'sigmoid'))
    # Compiling the CNN
    classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
    # Part 2 - Fitting the CNN to the images
    #from keras.preprocessing.image import ImageDataGenerator
    train_datagen = ImageDataGenerator(rescale = 1./255,
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True)
    test_datagen = ImageDataGenerator(rescale = 1./255)
    training_set = train_datagen.flow_from_directory("C:/Users/Admin/Desktop/malig_benign/training",
    target_size = (100, 100),
    batch_size = 32,
    class_mode = 'binary')
    test_set = test_datagen.flow_from_directory('C:/Users/Admin/Desktop/malig_benign/testing',
    target_size = (100, 100),
    batch_size = 32,
    class_mode = 'binary')
    classifier.fit_generator(training_set,
    steps_per_epoch = 10,
    epochs = 10,
    validation_data = test_set,
    validation_steps = 10)
    # Part 3 - Making new predictions
    #import numpy as np
    #from tkinter import *
    #from tkinter import filedialog

    #from keras.preprocessing import image
    test_image = image.load_img(test_image, target_size = (100, 100))
    test_image = image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis = 0)
    result = classifier.predict(test_image)
    training_set.class_indices
    if result[0][0] == 0:
        prediction = 'malignant'    
    else :
        prediction = 'benign'

    print (prediction)

我什至尝试在第二个神经网络中更改变量的名称，但仍然没有得到最终输出。我收到此错误： 

AttributeError                            Traceback (most recent call last)
~\Anaconda3\envs\tensorflow\lib\site-packages\PIL\Image.py in open(fp, mode)
   2546     try:
-> 2547         fp.seek(0)
   2548     except (AttributeError, io.UnsupportedOperation):

AttributeError: 'numpy.ndarray' object has no attribute 'seek'

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
<ipython-input-2-d9dc0d704b3a> in <module>()
    110 
    111     from keras.preprocessing import image
--> 112     test_image = image.load_img(test_image, target_size = (100, 100))
    113     test_image = image.img_to_array(test_image)
    114     test_image1= np.expand_dims(test_image, axis = 0)

~\Anaconda3\envs\tensorflow\lib\site-packages\keras\preprocessing\image.py in load_img(path, grayscale, target_size, interpolation)
    360         raise ImportError('Could not import PIL.Image. '
    361                           'The use of `array_to_img` requires PIL.')
--> 362     img = pil_image.open(path)
    363     if grayscale:
    364         if img.mode != 'L':

~\Anaconda3\envs\tensorflow\lib\site-packages\PIL\Image.py in open(fp, mode)
   2547         fp.seek(0)
   2548     except (AttributeError, io.UnsupportedOperation):
-> 2549         fp = io.BytesIO(fp.read())
   2550         exclusive_fp = True
   2551 

AttributeError: 'numpy.ndarray' object has no attribute 'read'

任何人都可以请教我如何解决，或者这是实现相同的另一种更好的方法。

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