Brain tumour detection using deep learning and machine learning part -2

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Brain tumour detection using deep learning and machine learning part -2

Contd/- PART-2

Convert the data in the mask column from integer to string format as we will require the data in string format.

brain_df_train['mask'] = brain_df_train['mask'].apply(lambda x: str(x))
brain_df_train.info()

As you can see, now each feature has the datatype as an object.

Split the data into train and test sets.

# split the data into train and test data
from sklearn.model_selection import train_test_split
train, test = train_test_split(brain_df_train, test_size = 0.15)

Augment more data using ImageDataGenerator. ImageDataGenerator generates batches of tensor image data with real-time data augmentation.

Refer here for more information on ImageDataGenerator and the parameters in detail.

We will create a train_generator and validation_generator from train data and a test_generator from test data.

# create an image generator
from keras_preprocessing.image import ImageDataGenerator

#Create a data generator which scales the data from 0 to 1 and makes validation split of 0.15
datagen = ImageDataGenerator(rescale=1./255., validation_split = 0.15)

train_generator=datagen.flow_from_dataframe(
dataframe=train,
directory= './',
x_col='image_path',
y_col='mask',
subset="training",
batch_size=16,
shuffle=True,
class_mode="categorical",
target_size=(256,256))

valid_generator=datagen.flow_from_dataframe(
dataframe=train,
directory= './',
x_col='image_path',
y_col='mask',
subset="validation",
batch_size=16,
shuffle=True,
class_mode="categorical",
target_size=(256,256))

# Create a data generator for test images
test_datagen=ImageDataGenerator(rescale=1./255.)

test_generator=test_datagen.flow_from_dataframe(
dataframe=test,
directory= './',
x_col='image_path',
y_col='mask',
batch_size=16,
shuffle=False,
class_mode='categorical',
target_size=(256,256))

Now, we will learn the concept of Transfer Learning and ResNet50 Model which will be used for further training model.

Transfer Learning as the name suggests, is a technique to use the pre-trained models in your training. You can build your model on top of this pre-trained model. This is a process that helps you decrease the development time and increase performance.

ResNet (Residual Network) is the ANN trained on the ImageNet dataset that can be used to train the model on top of it. ResNet50 is the variant of the ResNet model which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer.

Here, we are using the ResNet50 Model which is a Transfer Learning Model. Using this, we will further add more layers to build our model.

# Get the ResNet50 base model (Transfer Learning)
basemodel = ResNet50(weights = 'imagenet', include_top = False, input_tensor = Input(shape=(256, 256, 3)))
basemodel.summary()

You can view the layers in the resnet50 model by using .summary() as shown above.

Freeze the model weights. It means we will keep the weights constant so that it does not update further. This will avoid destroying any information during further training.

# freeze the model weights
for layer in basemodel.layers:
  layers.trainable = False

Now, as stated above we will add more layers on the top of the layers of ResNet50. These layers will learn to turn the old features into predictions on our dataset.

headmodel = basemodel.output
headmodel = AveragePooling2D(pool_size = (4,4))(headmodel)
headmodel = Flatten(name= 'flatten')(headmodel)
headmodel = Dense(256, activation = "relu")(headmodel)
headmodel = Dropout(0.3)(headmodel)
headmodel = Dense(256, activation = "relu")(headmodel)
headmodel = Dropout(0.3)(headmodel)
headmodel = Dense(256, activation = "relu")(headmodel)
headmodel = Dropout(0.3)(headmodel)
headmodel = Dense(2, activation = 'softmax')(headmodel)

model = Model(inputs = basemodel.input, outputs = headmodel)
model.summary()

These layers are added and you can see them in the summary.

Pooling layers are used to reduce the dimensions of the feature maps. The Average Pooling layer returns the average of the values.
Flatten layers convert our data into a vector.
A dense layer is the regular deeply connected neural network layer. Basically, it takes input and calculates output = activation(dot(input, kernel) + bias)
The dropout layer prevents the model from overfitting. It randomly sets the input units of hidden layers to 0 during training.

Compile the above-built model. Compile defines the loss function, the optimizer, and the metrics.

# compile the model
model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics= ["accuracy"])

Performing early stopping to save the best model with the least validation loss. Early stopping performs a large number of training epochs and stops training once the model performance does not further improve on the validation dataset.

Read more about EarlyStopping.

ModelCheckpoint callback is used with training using model.fit() to save the weights at some interval, so the weights can be loaded later to continue the training from the state saved.

Read more about ModelCheckpoint parameters.

# use early stopping to exit training if validation loss is not decreasing even after certain epochs
earlystopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)

# save the model with least validation loss
checkpointer = ModelCheckpoint(filepath="classifier-resnet-weights.hdf5", verbose=1, save_best_only=True)

Now, you train the model and give the callbacks defined above in the parameter.

model.fit(train_generator, steps_per_epoch= train_generator.n // 16, epochs = 1, validation_data= valid_generator, validation_steps= valid_generator.n // 16, callbacks=[checkpointer, earlystopping])

Predict and convert the predicted data into a list.

# make prediction
test_predict = model.predict(test_generator, steps = test_generator.n // 16, verbose =1)

# Obtain the predicted class from the model prediction
predict = []
for i in test_predict:
  predict.append(str(np.argmax(i)))
predict = np.asarray(predict)

Measure the accuracy of the model.

# Obtain the accuracy of the model
from sklearn.metrics import accuracy_score

accuracy = accuracy_score(original, predict)
accuracy

Print the classification report.

from sklearn.metrics import classification_report
report = classification_report(original, predict, labels = [0,1])
print(report)

So far so good!

Now, you can pat yourself as you have just completed the first part i.e., brain tumor detection. Now, we will head towards brain tumor segmentation in the second part of this series.

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