Upload CNN_segmented_tumour_deepfeatures.py

CNN_segmented_tumour_deepfeatures.py

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+import os
+import nibabel as nib
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization, MaxPooling2D, Flatten, Dense
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.preprocessing import image
+
+# Define the CNN model architecture
+model = Sequential()
+
+# First convolutional layer
+model.add(Conv2D(96, (9, 9), strides=(4, 4), padding='valid', input_shape=(224, 224, 1)))
+model.add(Activation('relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
+
+# Second convolutional layer
+model.add(Conv2D(256, (7, 7), strides=(1, 1), padding='same'))
+model.add(Activation('relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
+
+# Third convolutional layer
+model.add(Conv2D(384, (3, 3), strides=(1, 1), padding='same'))
+model.add(Activation('relu'))
+
+# Fourth convolutional layer
+model.add(Conv2D(384, (3, 3), strides=(1, 1), padding='same'))
+model.add(Activation('relu'))
+
+# Fifth convolutional layer
+model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same'))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
+
+# Flatten the output of the last pooling layer
+model.add(Flatten())
+
+# Fully connected layer
+model.add(Dense(4096))
+model.add(Activation('relu'))  # Add activation function
+
+# Compile the model
+model.compile(optimizer='adam', loss='categorical_crossentropy')
+
+# Define the directory that contains the patient folders
+base_dir = r"C:\Users\pg22\OneDrive - King's College London\Documents\PhD Data\UCSF-PDGM-v3\UCSF_PDGM"  # Replace with the actual path
+
+# Loop over each patient ID folder
+for folder_name in os.listdir(base_dir):
+    patient_dir = os.path.join(base_dir, folder_name)
+
+    # Check if it's a directory
+    if os.path.isdir(patient_dir):
+        # Extract the patient ID from the folder name and drop '_nifti'
+        patient_id = folder_name.replace('_nifti', '')
+
+        # Load the segmented tumor file
+        tumor_file = os.path.join(patient_dir, f'{patient_id}_tumor_segmentation.nii.gz')
+        tumor_data = nib.load(tumor_file).get_fdata()
+
+        # Preprocess the tumor data to match the input requirements of the CNN model
+        # Resize the image to the expected input size of the model (224, 224 in this case)
+        tumor_data_resized = np.array([image.array_to_img(slice_2d[..., None], scale=False).resize((224, 224)) for slice_2d in tumor_data])
+        # Normalize the pixel values to 0-1 range
+        tumor_data_normalized = tumor_data_resized / np.max(tumor_data_resized)
+        # Expand the dimensions of the image array if the model expects a 4D input (batch size, height, width, channels)
+        tumor_data_expanded = np.expand_dims(tumor_data_normalized, axis=-1)
+
+        # Pass the preprocessed data through the CNN model to extract deep features
+        features = model.predict(tumor_data_expanded)
+
+import matplotlib.pyplot as plt
+
+# Reshape the features into a 2D array for visualization
+# Calculate the correct dimensions
+dim1 = 64  # This can be any number that divides 983040 evenly
+dim2 = features.size // dim1
+
+# Reshape the features into a 2D array for visualization
+features_reshaped = features.reshape((dim1, dim2))
+
+# Use Matplotlib to display the image
+plt.imshow(features_reshaped, cmap='viridis')
+plt.colorbar()
+plt.show()
+# The number of features may vary depending on your model architecture
+# Here we assume the features are of shape (1, 4096)
+
+# https://www.mdpi.com/2075-4418/12/4/1018
+
+#save the feature file# Define the file path where you want to save the features
+features_file_path = r"C:\Users\pg22\OneDrive - King's College London\Documents\PhD Data\features.npy"
+
+# Save the features to the file
+np.save(features_file_path, features)
+