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Brain Tumor Detection and Classification
Brain Tumor Detection and Classification in python source code convolutional Neural Network (CNN) 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)
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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91._0_1255.jpg augmented data/no aug_7 no_0_4075.jpg augmented data/no aug_No11_0_3779.jpg augmented data/no aug_15 no_0_2133.jpg augmented data/no aug_13 no_0_3357.jpg augmented data/no aug_11 no_0_4129.jpg augmented data/no aug_No20_0_453.jpg augmented data/no aug_11 no_0_4911.jpg augmented data/no aug_7 no_0_6071.jpg augmented data/no aug_No15_0_6052.jpg augmented data/no aug_44no_0_7107.jpg augmented data/no aug_47 no_0_7539.jpg augmented data/no aug_22 no_0_8425.jpg augmented data/no aug_7 no_0_6343.jpg augmented data/no aug_45 no_0_9713.jpg augmented data/no aug_45 no_0_6484.jpg augmented data/no aug_39 no_0_1205.jpg augmented data/no aug_N5_0_7003.jpg augmented data/no aug_N20_0_3404.jpg augmented data/no aug_no 8_0_7756.jpg augmented data/no aug_4 no_0_4572.jpg augmented data/no aug_33 no_0_6062.jpg augmented data/no aug_no 923_0_3258.jpg augmented data/no aug_27 no_0_3315.jpg augmented data/no aug_N21_0_36.jpg augmented data/no aug_no 99_0_1449.jpg augmented data/no aug_N15_0_2727.jpg augmented data/no aug_N3_0_6992.jpg augmented data/no aug_N20_0_5488.jpg augmented data/no aug_17 no_0_740.jpg augmented data/no aug_1 no._0_1102.jpg augmented data/no aug_41 no_0_6374.jpg augmented data/no aug_46 no_0_3172.jpg augmented data/no aug_19 no_0_5986.jpg augmented data/no aug_no 9_0_720.jpg augmented data/no aug_6 no_0_9704.jpg augmented data/no aug_No21_0_4892.jpg augmented data/no aug_N16_0_647.jpg augmented data/no aug_no 6_0_6157.jpg augmented data/no aug_20 no_0_5063.jpg augmented data/no aug_no 7._0_1074.jpg augmented data/no aug_8 no_0_7915.jpg augmented data/no aug_No16_0_2676.jpg augmented data/no aug_39 no_0_9680.jpg augmented data/no aug_13 no_0_6526.jpg augmented data/no aug_no 7._0_8330.jpg augmented data/no aug_26 no_0_3441.jpg augmented data/no aug_no 5._0_3336.jpg augmented data/no aug_N16_0_7462.jpg augmented data/no aug_34 no_0_2428.jpg augmented data/no aug_No19_0_6780.jpg augmented data/no aug_1 no._0_7980.jpg augmented data/no aug_no 6_0_6128.jpg augmented data/no aug_17 no_0_3243.jpg augmented data/no aug_2 no._0_5418.jpg augmented data/no aug_N11_0_4754.jpg augmented data/no aug_N3_0_352.jpg augmented data/no aug_21 no_0_3375.jpg augmented data/no aug_N3_0_8838.jpg augmented data/no aug_18 no_0_8769.jpg augmented data/no aug_27 no_0_9639.jpg augmented data/no aug_50 no_0_4785.jpg augmented data/no aug_31 no_0_4219.jpg augmented data/no aug_4 no_0_9150.jpg augmented data/no aug_No11_0_6281.jpg augmented data/no aug_10 no_0_2521.jpg augmented data/no aug_N17_0_6246.jpg augmented data/no aug_25 no_0_211.jpg augmented data/no aug_27 no_0_1521.jpg augmented data/no aug_no 92_0_9612.jpg augmented data/no aug_N5_0_293.jpg augmented data/no aug_No20_0_6802.jpg augmented data/no aug_24 no_0_3936.jpg augmented data/no aug_20 no_0_657.jpg augmented data/no aug_no 90_0_19.jpg augmented data/no aug_49 no_0_7617.jpg augmented data/no aug_41 no_0_4813.jpg augmented data/no aug_29 no_0_8820.jpg augmented data/no aug_no 6_0_3556.jpg augmented data/no aug_no 4_0_1721.jpg augmented data/no aug_N6_0_6281.jpg augmented data/no aug_3 no_0_3302.jpg augmented data/no aug_13 no_0_1261.jpg augmented data/no aug_29 no_0_1670.jpg augmented data/no aug_33 no_0_2144.jpg augmented data/no aug_No14_0_5497.jpg augmented data/no aug_2 no._0_5204.jpg augmented data/no aug_43 no_0_9379.jpg augmented data/no aug_No19_0_7239.jpg augmented data/no 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)
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)
In [20]:
model.metrics_names
Out[20]:
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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