--- a
+++ b/helper.py
@@ -0,0 +1,153 @@
+import matplotlib.pyplot as plt
+import nibabel as nib
+import numpy as np
+import random
+from scipy import ndimage
+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Conv3D, MaxPool3D, BatchNormalization, GlobalAveragePooling3D, Dropout
+
+
+def read_scan(filepath):
+    """Read and load volume"""
+    # Read file
+    scan = nib.load(filepath)
+    # Get raw data
+    scan = scan.get_fdata()
+    return scan
+
+
+def normalize(volume):
+    """Normalize the volume"""
+    min = -1000
+    max = 400
+    volume[volume < min] = min
+    volume[volume > max] = max
+    volume = (volume - min) / (max - min)
+    volume = volume.astype("float32")
+    return volume
+
+
+def resize_volume(img):
+    """Resize across z-axis"""
+    # Set the desired depth
+    desired_depth = 64
+    desired_width = 128
+    desired_height = 128
+    # Get current depth
+    current_depth = img.shape[-1]
+    current_width = img.shape[0]
+    current_height = img.shape[1]
+    # Compute depth factor
+    depth = current_depth / desired_depth
+    width = current_width / desired_width
+    height = current_height / desired_height
+    depth_factor = 1 / depth
+    width_factor = 1 / width
+    height_factor = 1 / height
+    # Rotate
+    img = ndimage.rotate(img, 90, reshape=False)
+    # Resize across z-axis
+    img = ndimage.zoom(img, (width_factor, height_factor, depth_factor), order=1)
+    return img
+
+
+def process_scan(path):
+    """Read and resize volume"""
+    # Read scan
+    volume = read_scan(path)
+    # Normalize
+    volume = normalize(volume)
+    # Resize width, height and depth
+    volume = resize_volume(volume)
+    return volume
+
+@tf.function
+def rotate(volume):
+    """Rotate the volume by a few degrees"""
+
+    def scipy_rotate(volume):
+        # define some rotation angles
+        angles = [-20, -10, -5, 5, 10, 20]
+        # pick angles at random
+        angle = random.choice(angles)
+        # rotate volume
+        volume = ndimage.rotate(volume, angle, reshape=False)
+        volume[volume < 0] = 0
+        volume[volume > 1] = 1
+        return volume
+
+    augmented_volume = tf.numpy_function(scipy_rotate, [volume], tf.float32)
+    return augmented_volume
+
+
+def train_preprocessing(volume, label):
+    """Process training data by rotating and adding a channel."""
+    # Rotate volume
+    volume = rotate(volume)
+    volume = tf.expand_dims(volume, axis=3)
+    return volume, label
+
+
+def validation_preprocessing(volume, label):
+    """Process validation data by only adding a channel."""
+    volume = tf.expand_dims(volume, axis=3)
+    return volume, label
+
+
+
+
+def plot_slices(num_rows, num_columns, width, height, data):
+    """Plot a montage of 20 CT slices"""
+    data = np.rot90(np.array(data))
+    data = np.transpose(data)
+    data = np.reshape(data, (num_rows, num_columns, width, height))
+    rows_data, columns_data = data.shape[0], data.shape[1]
+    heights = [slc[0].shape[0] for slc in data]
+    widths = [slc.shape[1] for slc in data[0]]
+    fig_width = 12.0
+    fig_height = fig_width * sum(heights) / sum(widths)
+    f, axarr = plt.subplots(
+        rows_data,
+        columns_data,
+        figsize=(fig_width, fig_height),
+        gridspec_kw={"height_ratios": heights},
+    )
+    for i in range(rows_data):
+        for j in range(columns_data):
+            axarr[i, j].imshow(data[i][j], cmap="gray")
+            axarr[i, j].axis("off")
+    plt.subplots_adjust(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)
+    plt.show()
+
+
+def build_model(width=128, height=128, depth=64):
+    """Build a 3D convolutional neural network model."""
+
+    inputs = tf.keras.Input((width, height, depth, 1))
+
+    x = Conv3D(filters=64, kernel_size=3, activation="relu")(inputs)
+    x = MaxPool3D(pool_size=2)(x)
+    x = BatchNormalization()(x)
+
+    x = Conv3D(filters=64, kernel_size=3, activation="relu")(x)
+    x = MaxPool3D(pool_size=2)(x)
+    x = BatchNormalization()(x)
+
+    x = Conv3D(filters=128, kernel_size=3, activation="relu")(x)
+    x = MaxPool3D(pool_size=2)(x)
+    x = BatchNormalization()(x)
+
+    x = Conv3D(filters=256, kernel_size=3, activation="relu")(x)
+    x = MaxPool3D(pool_size=2)(x)
+    x = BatchNormalization()(x)
+
+    x = GlobalAveragePooling3D()(x)
+    x = Dense(units=512, activation="relu")(x)
+    x = Dropout(0.3)(x)
+
+    outputs = Dense(units=1, activation="sigmoid")(x)
+
+    # Define the model.
+    model = tf.keras.Model(inputs, outputs, name="3dctscan")
+    return model
+