What is Transfer Learning?

Let’s begin by explaining transfer learning. This method involves leveraging a pre-trained model as a foundation for training a new model. Essentially, we utilize the pre-trained model to extract features from images and subsequently train a new model based on those features to accomplish a specific task. This approach can significantly reduce time and resource investment, especially when working with limited datasets.

Step 1: Data Collection and Preparation

To create our brain tumor detection model, we first need to gather a dataset of brain MRI scans. We will utilize the “Brain Tumor MRI” dataset available on Kaggle, which contains 253 brain MRI scans, each labeled to indicate the presence or absence of a tumor.

The initial step involves downloading the dataset and extracting the images. We'll employ the Pydicom library to read the DICOM files and extract the pixel data from the images. After that, we will resize the images to a standardized size and convert them to grayscale to minimize the data we need to process.

import os

import numpy as np

import pydicom

from skimage.transform import resize

import matplotlib.pyplot as plt

data_dir = 'path/to/dataset'

output_dir = 'path/to/output'

img_size = (224, 224) # Desired image size

def preprocess_data():

for subdir, _, files in os.walk(data_dir):

for file in files:

if '.dcm' in file:

filepath = os.path.join(subdir, file)

filename = file.replace('.dcm', '.jpg')

img = pydicom.dcmread(filepath).pixel_array

img = resize(img, img_size, anti_aliasing=True)

img = (img * 255).astype(np.uint8)

img_path = os.path.join(output_dir, filename)

plt.imsave(img_path, img, cmap='gray')

Step 2: Building the Transfer Learning Model

Next, we will construct our transfer learning model using a pre-trained CNN. We will use the VGG16 model, a popular CNN for image classification tasks. The last layer of the model will be removed, and we will add a new layer that performs binary classification (tumor vs. non-tumor).

from keras.applications import VGG16

from keras.models import Sequential, Model

from keras.layers import Dense, Flatten

def build_model():

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

x = base_model.output

x = Flatten()(x)

predictions = Dense(1, activation='sigmoid')(x)

model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:

layer.trainable = False

return model

Step 3: Training the Model

We will split the dataset into training and validation sets and train our model using the Adam optimizer along with a binary cross-entropy loss function. To enhance the variety of our training set, we will also implement data augmentation techniques such as rotation and flipping.

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(

rotation_range=10,

horizontal_flip=True,

vertical_flip=True,

rescale=1./255,

validation_split=0.2)

train_generator = train_datagen.flow_from_directory(

output_dir,

target_size=img_size,

batch_size=32,

class_mode='binary',

subset='training')

validation_generator = train_datagen.flow_from_directory(

output_dir,

target_size=img_size,

batch_size=32,

class_mode='binary',

subset='validation')

model = build_model()

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit_generator(

train_generator,

steps_per_epoch=train_generator.samples // train_generator.batch_size,

epochs=10,

validation_data=validation_generator,

validation_steps=validation_generator.samples // validation_generator.batch_size)

model.save('tumor_detection_model.h5')

Step 4: Developing the Web Application

With our model ready, we can now create the web application using Flask, a Python web framework. We’ll begin by setting up a new Flask application and defining a route to upload an MRI scan.

from flask import Flask, request, render_template

from keras.models import load_model

from keras.preprocessing.image import load_img, img_to_array

app = Flask(__name__)

model = load_model('tumor_detection_model.h5')

model._make_predict_function() # Fix for TensorFlow issue with Flask

@app.route('/', methods=['GET', 'POST'])

def upload_file():

if request.method == 'POST':

file = request.files['file']

img = load_img(file, target_size=img_size, color_mode='grayscale')

img = img_to_array(img) / 255

img = np.expand_dims(img, axis=0)

prediction = model.predict(img)

result = 'No Tumor Detected' if prediction[0] < 0.5 else 'Tumor Detected'

return render_template('result.html', result=result)

else:

return render_template('index.html')

In this code, we defined a route for the homepage (‘/’) and a route for file upload. When a user uploads an MRI scan, the file is processed and passed through our trained model for prediction, which is then displayed on a new page.

Step 5: Deploying the Application

To deploy our web application, we will use Heroku, a cloud platform for hosting web apps. We need to create a new Heroku app and push our code to the Heroku git repository. Additionally, we will define a Procfile to specify how to run our application using Gunicorn, a Python web server.

web: gunicorn app:app

Finally, we will set up a PostgreSQL database using the Heroku Postgres add-on to manage user-uploaded MRI scans.