Identifying Alzheimer’s Disease with Deep Learning: A Transfer Learning Approach
Alzheimer’s disease is a degenerative brain disorder that affects millions of people worldwide. It is a progressive disease that leads to memory loss, cognitive decline, and eventually the inability to carry out basic tasks. Early diagnosis and intervention can improve the quality of life of those affected by the disease. In this tutorial, we will use deep learning techniques to identify Alzheimer’s disease from MRI brain scans.
We will be using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset for this tutorial. The dataset contains MRI brain scans of patients with Alzheimer’s disease and healthy individuals. We will use the T1-weighted MRI images for our analysis.
First, we will load the dataset and split it into training and testing sets. We will also preprocess the data by resizing the images and normalizing the pixel values.
# Import the necessary libraries import os import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from tensorflow.keras.preprocessing.image import load_img, img_to_array from tensorflow.keras.utils import to_categorical from tensorflow.keras.applications.mobilenet_v2 import preprocess_input # Load the metadata file metadata = pd.read_csv('ADNI_Metadata.csv') # Create lists to store the images and labels images =  labels =  # Loop through the metadata file and load the images and labels for i, row in metadata.iterrows(): # Load the image and resize it to 224x224 img = load_img(row['Image'], target_size=(224, 224)) img_array = img_to_array(img) # Preprocess the image img_array = preprocess_input(img_array) images.append(img_array) # Add the label to the list label = row['Label'] if label == 'CN': labels.append(0) elif label == 'AD': labels.append(1) # Convert the data to arrays images = np.array(images) labels = np.array(labels) # Split the data into training and testing sets train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.2, random_state=42)
Building the Model
We will use transfer learning to build our model. We will use the MobileNetV2 architecture, which has been pre-trained on the ImageNet dataset. We will add a GlobalAveragePooling2D layer to reduce the dimensionality of the output and a Dense layer with a sigmoid activation function to classify the images as Alzheimer’s disease or healthy.
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2 from tensorflow.keras.models import Model from tensorflow.keras.layers import GlobalAveragePooling2D, Dense # Load the pre-trained MobileNetV2 model base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) # Add a GlobalAveragePooling2D layer x = base_model.output x = GlobalAveragePooling2D()(x) # Add a Dense layer with a sigmoid activation function output = Dense(1, activation='sigmoid')(x) # Create the model model = Model(inputs=base_model.input, outputs=output) # Freeze the layers of the pre-trained model for layer in base_model.layers: layer.trainable = False # Compile the model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
Training the Model
We will train the model using the training data and evaluate it on the testing data. We will use the binary cross-entropy loss function and the Adam optimizer.
# Train the model history = model.fit(train_images, train_labels, epochs=10, batch_size=32, validation_data=(test_images, test_labels)) # Evaluate the model on the testing data test_loss, test_acc = model.evaluate(test_images, test_labels) print('Test accuracy:', test_acc)
Predicting Alzheimer’s Disease
We can now use our trained model to predict Alzheimer’s disease from MRI brain scans. We will load a sample image and preprocess it before making a prediction.
# Load a sample image img_path = 'sample_image.jpg' img = load_img(img_path, target_size=(224, 224)) img_array = img_to_array(img) img_array = preprocess_input(img_array) img_array = np.expand_dims(img_array, axis=0) # Make a prediction prediction = model.predict(img_array) # Print the prediction if prediction < 0.5: print('The image is classified as healthy.') else: print('The image is classified as Alzheimer\'s disease.')
In this tutorial, we have learned how to use deep learning techniques to identify Alzheimer’s disease from MRI brain scans. We used transfer learning with the MobileNetV2 architecture and achieved good accuracy on the testing data. This technique can be applied to other medical imaging datasets to aid in the early detection and diagnosis of diseases.
Lyron Foster is a Hawai’i based African American Author, Musician, Actor, Blogger, Philanthropist and Multinational Serial Tech Entrepreneur.