I have used a 3D CNN architecture, for detecting the presence of a particular promoter (MGMT), by using FLAIR brain scans. (64 slices per patient). The output is supposed to be binary (0/1).

I have gone through the pre-processing properly, and used stratification after splitting the "train" dataset into train and validation sets, (80-20 ratio). My model initialisation and training kernels look like this:

def get_model(width=128, height=128, depth=64):
"""Build a 3D convolutional neural network model."""

inputs = keras.Input((width, height, depth, 1))

x = layers.Conv3D(filters=64, kernel_size=3, activation="relu")(inputs)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)

x = layers.Conv3D(filters=64, kernel_size=3, activation="relu")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)

x = layers.Conv3D(filters=128, kernel_size=3, activation="relu")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)

x = layers.Conv3D(filters=256, kernel_size=3, activation="relu")(x)
x = layers.MaxPool3D(pool_size=2)(x)
x = layers.BatchNormalization()(x)

x = layers.GlobalAveragePooling3D()(x)
x = layers.Dense(units=512, activation="relu")(x)
x = layers.Dropout(0.3)(x)

outputs = layers.Dense(units=1, activation="sigmoid")(x)

# Define the model.
model = keras.Model(inputs, outputs, name="3dcnn")
return model

# Build model.
model = get_model(width=128, height=128, depth=64)

Compile model:

initial_learning_rate = 0.0001
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True

# Define callbacks.
checkpoint_cb = keras.callbacks.ModelCheckpoint(
    "Brain_3d_classification.h5", save_best_only=True,monitor = 'val_acc', 
                             mode = 'max', verbose = 1
early_stopping_cb = keras.callbacks.EarlyStopping(monitor="val_acc", patience=20,mode = 'max', verbose = 1,
                           restore_best_weights = True)

# Train the model, doing validation at the end of each epoch
epochs = 60
    callbacks=[checkpoint_cb, early_stopping_cb],

This is my first time ever working with a 3D CNN, and I used this keras webpage for the format:https://keras.io/examples/vision/3D_image_classification/

The (max) validation accuracy in my case was about 54%. I tried reducing the initial learning rate , and for 0.00001 I got to a max of 66.7%. For learning rates of 0.00005, 0.00002, I got max accuracy of about 60 and 62%.

Accuracy vs epoch plots for learning rates 0.0001, 0.00005,0.00002 and 0.00001:

0.0001 enter image description here enter image description here enter image description here

It does seem like reducing the initial learning rate has a positive effect on accuracy, although the accuracy is still very low.

What other parameters can I tune to expect a better accuracy? And is it okay to just keep reducing the initial learning rate until we achieve a targeted accuracy?

I know this is a rather broad question, but I am quite confused as to how we should approach increasing the accuracy in the case of CNNs, (that too 3D), where there just seems to be a lot of stuff going on. Do I change something in my initialisations? Add more layers? Or change the parameters? Do I decrease or increase them? With so many things going on, I don't think trying every combination and just keep repeating the training process is an efficient idea...

Full notebook (including pre-processing steps): https://www.kaggle.com/shivamee682003/3d-image-preprocessing-17cd03/edit

  • $\begingroup$ maybe more dropout layers, in general tuning architecture itself, via hyper-parameter search is possible $\endgroup$
    – Nikos M.
    Commented Oct 11, 2021 at 15:53

1 Answer 1


Adding more FC layers is also helpful for small data and small image sizes for example after flattening layer as below:

flatten_layer = layers.Flatten()(x)

dense_layer1 = layers.Dense(units=512, activation='relu')(flatten_layer)
dense_layer1 = layers.Dropout(0.4)(dense_layer1)

dense_layer2 = layers.Dense(units=512, activation='relu')(dense_layer1)
dense_layer2 = layers.Dropout(0.4)(dense_layer2)

dense_layer3 = layers.Dense(units=256, activation='relu')(dense_layer2)
dense_layer3 = layers.Dropout(0.4)(dense_layer3)

dense_layer4 = layers.Dense(units=128, activation='relu')(dense_layer3)
dense_layer4 = layers.Dropout(0.4)(dense_layer4)

You can play with dropout rate and the number of neurons in the Dense layer as there is no rule of thumb for them. The following paper might be helpful



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