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Brain Tumor Detection and Classification in python source code convolutional Neural Network (CNN) Deep learning Tensorflow top Keras implementation

Deep learning Tensorflow top keras implementation

In [1]:
import tensorflow as tf
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # -1  to USE CPU
from keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense,Dropout
from keras.models import Model, load_model
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.utils import shuffle
import cv2
import imutils
import numpy as np
import matplotlib.pyplot as plt
import time
from os import listdir

get_ipython().magic(u'matplotlib inline')
/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
In [2]:
def crop_brain_contour(image, plot=False):



    # Convert the image to grayscale, and blur it slightly
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)

    # Threshold the image, then perform a series of erosions +
    # dilations to remove any small regions of noise
    thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
    thresh = cv2.erode(thresh, None, iterations=2)
    thresh = cv2.dilate(thresh, None, iterations=2)

    # Find contours in thresholded image, then grab the largest one
    cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    c = max(cnts, key=cv2.contourArea)


    # Find the extreme points
    extLeft = tuple(c[c[:, :, 0].argmin()][0])
    extRight = tuple(c[c[:, :, 0].argmax()][0])
    extTop = tuple(c[c[:, :, 1].argmin()][0])
    extBot = tuple(c[c[:, :, 1].argmax()][0])

    # crop new image out of the original image using the four extreme points (left, right, top, bottom)
    new_image = image[extTop[1]:extBot[1], extLeft[0]:extRight[0]]

    if plot:
        plt.figure()

        plt.subplot(1, 2, 1)
        plt.imshow(image)

        plt.tick_params(axis='both', which='both',
                        top=False, bottom=False, left=False, right=False,
                        labelbottom=False, labeltop=False, labelleft=False, labelright=False)

        plt.title('Original Image')

        plt.subplot(1, 2, 2)
        plt.imshow(new_image)

        plt.tick_params(axis='both', which='both',
                        top=False, bottom=False, left=False, right=False,
                        labelbottom=False, labeltop=False, labelleft=False, labelright=False)

        plt.title('Cropped Image')

        plt.show()

    return new_image
In [3]:
ex_img = cv2.imread('yes/Y1.jpg')
ex_new_img = crop_brain_contour(ex_img, True)
brain tumor detection
In [4]:
def load_data(dir_list, image_size):
    """
    Read images, resize and normalize them. 
    Arguments:
        dir_list: list of strings representing file directories.
    Returns:
        X: A numpy array with shape = (#_examples, image_width, image_height, #_channels)
        y: A numpy array with shape = (#_examples, 1)
    """

    # load all images in a directory
    X = []
    y = []
    image_width, image_height = image_size

    for directory in dir_list:
        for filename in listdir(directory):
            print(filename)
            print(directory)
            # load the image
            image = cv2.imread(directory + '/' + filename)
            # print(image)
            # crop the brain and ignore the unnecessary rest part of the image
            image = crop_brain_contour(image, plot=False)
            # resize image
            image = cv2.resize(image, dsize=(image_width, image_height), interpolation=cv2.INTER_CUBIC)
            # normalize values
            image = image / 255.
            # convert image to numpy array and append it to X
            X.append(image)
            # append a value of 1 to the target array if the image
            # is in the folder named 'yes', otherwise append 0.
            if directory[-3:] == 'yes':
                y.append([1])
            else:
                y.append([0])

    X = np.array(X)
    y = np.array(y)

    # Shuffle the data
    X, y = shuffle(X, y)
    print('Number of examples is:{}'.format(len(X)))
    return X,y
In [5]:
augmented_path = 'augmented data/'

# augmented data (yes and no) contains both the original and the new generated examples
augmented_yes = augmented_path + 'yes'
augmented_no = augmented_path + 'no'

IMG_WIDTH, IMG_HEIGHT = (240, 240)

X, y = load_data([augmented_yes, augmented_no], (IMG_WIDTH, IMG_HEIGHT))
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Number of examples is:2065
In [6]:
def plot_sample_images(X, y, n=50):

    for label in [0,1]:
        # grab the first n images with the corresponding y values equal to label
        images = X[np.argwhere(y == label)]
        n_images = images[:n]

        columns_n = 10
        rows_n = int(n/ columns_n)

        plt.figure(figsize=(20, 10))

        i = 1 # current plot        
        for image in n_images:
            plt.subplot(rows_n, columns_n, i)
            plt.imshow(image[0])

            # remove ticks
            plt.tick_params(axis='both', which='both',
                            top=False, bottom=False, left=False, right=False,
                           labelbottom=False, labeltop=False, labelleft=False, labelright=False)

            i += 1

        label_to_str = lambda label: "Yes" if label == 1 else "No"
        plt.suptitle("Brain Tumor: {}".format(label_to_str(label)))
        plt.show()

plot_sample_images(X, y)
brain tumor no
brain tumor yes
In [7]:
def split_data(X, y, test_size=0.2):

    X_train, X_test_val, y_train, y_test_val = train_test_split(X, y, test_size=test_size)
    X_test, X_val, y_test, y_val = train_test_split(X_test_val, y_test_val, test_size=0.5)

    return X_train, y_train, X_val, y_val, X_test, y_test
In [8]:
X_train, y_train, X_val, y_val, X_test, y_test = split_data(X, y, test_size=0.3)
In [9]:
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of development examples = " + str(X_val.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(y_train.shape))
print ("X_val (dev) shape: " + str(X_val.shape))
print ("Y_val (dev) shape: " + str(y_val.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(y_test.shape))
number of training examples = 1445
number of development examples = 310
number of test examples = 310
X_train shape: (1445, 240, 240, 3)
Y_train shape: (1445, 1)
X_val (dev) shape: (310, 240, 240, 3)
Y_val (dev) shape: (310, 1)
X_test shape: (310, 240, 240, 3)
Y_test shape: (310, 1)
In [10]:
def hms_string(sec_elapsed):
    h = int(sec_elapsed / (60 * 60))
    m = int((sec_elapsed % (60 * 60)) / 60)
    s = sec_elapsed % 60
    return "{}:{}:{}".format(h,m,round(s,1))
In [11]:
def compute_f1_score(y_true, prob):
    # convert the vector of probabilities to a target vector
    y_pred = np.where(prob > 0.5, 1, 0)

    score = f1_score(y_true, y_pred)

    return score
In [12]:
def build_model(input_shape):
    # Define the input placeholder as a tensor with shape input_shape. 
    X_input = Input(input_shape)
    X = Conv2D(64, (3, 3), strides = (1, 1), name = 'conv0')(X_input)
    X = BatchNormalization(axis = 3, name = 'bn0')(X)
    X = Activation('relu')(X) # 

    # MAXPOOL
    X = MaxPooling2D((2, 2), name='max_pool0')(X) # 
    # X=Dropout(0.50)(X)


    X = Conv2D(128, (5, 5), strides=(2, 2), name='conv1')(X)
    X = BatchNormalization(axis=3, name='bn1')(X)
    X = Activation('relu')(X)  # shape=(?, 238, 238, 32)
    # MAXPOOL
    X = MaxPooling2D((4, 4), name='max_pool1')(X) # shape=(?, 14, 14, 32)
    X=Dropout(0.50)(X)

    # FLATTEN X 
    X = Flatten()(X) # shape=(?, 6272)
    # FULLYCONNECTED
    X = Dense(1, activation='sigmoid', name='fc')(X) # shape=(?, 1)


    model = Model(inputs = X_input, outputs = X, name='BrainDetectionModel')

    return model
In [13]:
IMG_SHAPE = (IMG_WIDTH, IMG_HEIGHT, 3)


model = build_model(IMG_SHAPE)



model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         (None, 240, 240, 3)       0
_________________________________________________________________
conv0 (Conv2D)               (None, 238, 238, 64)      1792
_________________________________________________________________
bn0 (BatchNormalization)     (None, 238, 238, 64)      256
_________________________________________________________________
activation_1 (Activation)    (None, 238, 238, 64)      0
_________________________________________________________________
max_pool0 (MaxPooling2D)     (None, 119, 119, 64)      0
_________________________________________________________________
conv1 (Conv2D)               (None, 58, 58, 128)       204928
_________________________________________________________________
bn1 (BatchNormalization)     (None, 58, 58, 128)       512
_________________________________________________________________
activation_2 (Activation)    (None, 58, 58, 128)       0
_________________________________________________________________
max_pool1 (MaxPooling2D)     (None, 14, 14, 128)       0
_________________________________________________________________
dropout_1 (Dropout)          (None, 14, 14, 128)       0
_________________________________________________________________
flatten_1 (Flatten)          (None, 25088)             0
_________________________________________________________________
fc (Dense)                   (None, 1)                 25089
=================================================================
Total params: 232,577
Trainable params: 232,193
Non-trainable params: 384
_________________________________________________________________
In [14]:
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])


import time

# tensorboard
log_file_name = 'brain_tumor_detection_cnn_{}'.format(int(time.time()))
tensorboard = TensorBoard(log_dir='logs/{}'.format(log_file_name))




# checkpoint
# unique file name that will include the epoch and the validation (development) accuracy
filepath="cnn-parameters-improvement-{epoch:02d}-{val_acc:.2f}"
# save the model with the best validation (development) accuracy till now
checkpoint = ModelCheckpoint("models/{}.model".format(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max'))
In [15]:
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                              patience=5, min_lr=0.0001)
In [16]:
start_time = time.time()

model.fit(x=X_train, y=y_train, batch_size=32, epochs=30, validation_data=(X_val, y_val), callbacks=[reduce_lr,tensorboard, checkpoint])

end_time = time.time()
execution_time = (end_time - start_time)
print("Elapsed time: {}".format(hms_string(execution_time)))
Train on 1445 samples, validate on 310 samples
Epoch 1/30
1445/1445 [==============================] - 11s 7ms/step - loss: 1.0636 - acc: 0.6353 - val_loss: 0.6543 - val_acc: 0.7000
Epoch 2/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.5978 - acc: 0.7405 - val_loss: 0.6610 - val_acc: 0.7290
Epoch 3/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.5602 - acc: 0.7668 - val_loss: 0.9219 - val_acc: 0.6903
Epoch 4/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.5339 - acc: 0.7938 - val_loss: 0.7456 - val_acc: 0.7645
Epoch 5/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.4725 - acc: 0.8125 - val_loss: 0.5083 - val_acc: 0.8194
Epoch 6/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.4037 - acc: 0.8415 - val_loss: 0.6312 - val_acc: 0.8161
Epoch 7/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.3031 - acc: 0.8927 - val_loss: 1.8791 - val_acc: 0.5548
Epoch 8/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.3781 - acc: 0.8706 - val_loss: 1.1330 - val_acc: 0.6903
Epoch 9/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.4593 - acc: 0.8304 - val_loss: 1.3630 - val_acc: 0.7032
Epoch 10/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.3146 - acc: 0.8810 - val_loss: 0.4320 - val_acc: 0.8387
Epoch 11/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.2348 - acc: 0.9017 - val_loss: 0.4835 - val_acc: 0.8516
Epoch 12/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.2713 - acc: 0.9003 - val_loss: 0.4776 - val_acc: 0.8581
Epoch 13/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.2269 - acc: 0.9135 - val_loss: 0.3195 - val_acc: 0.8774
Epoch 14/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.3034 - acc: 0.8948 - val_loss: 0.4543 - val_acc: 0.8516
Epoch 15/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.6060 - acc: 0.8304 - val_loss: 0.4141 - val_acc: 0.8677
Epoch 16/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.1978 - acc: 0.9308 - val_loss: 0.4390 - val_acc: 0.8645
Epoch 17/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.1793 - acc: 0.9329 - val_loss: 2.1381 - val_acc: 0.6355
Epoch 18/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.1833 - acc: 0.9370 - val_loss: 0.9481 - val_acc: 0.7290
Epoch 19/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.1543 - acc: 0.9370 - val_loss: 0.3040 - val_acc: 0.9032
Epoch 20/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.1073 - acc: 0.9571 - val_loss: 0.3192 - val_acc: 0.8935
Epoch 21/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0821 - acc: 0.9751 - val_loss: 0.2403 - val_acc: 0.9065
Epoch 22/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0946 - acc: 0.9702 - val_loss: 0.2527 - val_acc: 0.9065
Epoch 23/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.1048 - acc: 0.9619 - val_loss: 0.2626 - val_acc: 0.9097
Epoch 24/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0942 - acc: 0.9640 - val_loss: 0.2869 - val_acc: 0.9000
Epoch 25/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0888 - acc: 0.9647 - val_loss: 0.2518 - val_acc: 0.9000
Epoch 26/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0869 - acc: 0.9640 - val_loss: 0.2390 - val_acc: 0.9129
Epoch 27/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0827 - acc: 0.9702 - val_loss: 0.2661 - val_acc: 0.9032
Epoch 28/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0745 - acc: 0.9737 - val_loss: 0.2588 - val_acc: 0.9097
Epoch 29/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0812 - acc: 0.9654 - val_loss: 0.2316 - val_acc: 0.9194
Epoch 30/30
1445/1445 [==============================] - 9s 6ms/step - loss: 0.0622 - acc: 0.9744 - val_loss: 0.2291 - val_acc: 0.9129
Elapsed time: 0:4:38.8
In [18]:
history = model.history.history

for key in history.keys():
    print(key)
acc
loss
val_acc
val_loss
lr
In [19]:
def plot_metrics(history):

    train_loss = history['loss']
    val_loss = history['val_loss']
    train_acc = history['acc']
    val_acc = history['val_acc']

    # Loss
    plt.figure()
    plt.plot(train_loss, label='Training Loss')
    plt.plot(val_loss, label='Validation Loss')
    plt.title('Loss')
    plt.legend()
    plt.show()

    # Accuracy
    plt.figure()
    plt.plot(train_acc, label='Training Accuracy')
    plt.plot(val_acc, label='Validation Accuracy')
    plt.title('Accuracy')
    plt.legend()
    plt.show()




plot_metrics(history)
training loss
training accuracy
In [20]:
model.metrics_names
Out[20]:
['loss', 'acc']
In [21]:
loss, acc = model.evaluate(x=X_test, y=y_test)
310/310 [==============================] - 1s 3ms/step
In [22]:
print ("Test Loss = {}".format(loss))
print ("Test Accuracy = {}".format(acc))
Test Loss = 0.274844214032
Test Accuracy = 0.916129034181
In [23]:
y_test_prob = model.predict(X_test)
In [24]:
# In[76]:


f1score = compute_f1_score(y_test, y_test_prob)
In [25]:
y_val_prob = model.predict(X_val)
In [26]:
# In[85]:


f1score_val = compute_f1_score(y_val, y_val_prob)
In [27]:
def data_percentage(y):

    m=len(y)
    n_positive = np.sum(y)
    n_negative = m - n_positive

    pos_prec = (n_positive* 100.0)/ m
    neg_prec = (n_negative* 100.0)/ m

    print("Number of examples:{}".format(m))
    print("Percentage of positive examples: {}%, number of pos examples: {}".format(pos_prec,n_positive))
    print("Percentage of negative examples: {}%, number of neg examples: {}".format(neg_prec,n_negative))
In [28]:
data_percentage(y)
Number of examples:2065
Percentage of positive examples: 52.5423728814%, number of pos examples: 1085
Percentage of negative examples: 47.4576271186%, number of neg examples: 980
In [29]:
print("Training Data:")
data_percentage(y_train)
print("Validation Data:")
data_percentage(y_val)
print("Testing Data:")
data_percentage(y_test)
Training Data:
Number of examples:1445
Percentage of positive examples: 52.6643598616%, number of pos examples: 761
Percentage of negative examples: 47.3356401384%, number of neg examples: 684
Validation Data:
Number of examples:310
Percentage of positive examples: 52.2580645161%, number of pos examples: 162
Percentage of negative examples: 47.7419354839%, number of neg examples: 148
Testing Data:
Number of examples:310
Percentage of positive examples: 52.2580645161%, number of pos examples: 162
Percentage of negative examples: 47.7419354839%, number of neg examples: 148

Learn to develop Brain tumor detection and classification system from the scracth from this video by RIS AI. In this notebook, you guys learn about Brain Tumor Detection | Brain Tumor Classification using CNN in Python. The CNN is derived from TensorFlow on the core side and implemented using Keras. You learn how to import images of ultra sound 2D brain images (X-ray or MRI) using pre-processing done using OpenCV.

Images are augmented and then Gaussian filter is applied to these. First, the image is converted into GreyScale and then into Gaussian blur filter. the process includes augmentation, dilation, image erosion and image thresholding. Then, image blur remover, image speckle noise removing, high dimensional feature analysis using CNN are implemented.

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Also, we use variable learning rate applied to avoid over fitting of our deep neural network. Tensor board log file also stored. Event monetization is also done. Further, the process proceeds in these steps: Optimizer Adam is used, loss, binary cross entropy loss, metric use accuracy, also plot training loss and validation loss and training accuracy, validation accuracy derived from model history. For the parameter analysis, we have used accuracy F1 score precision recall. Performance was good.

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