# Improving validation losses and accuracy for 3D CNN

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)
model.summary()


# 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
)
model.compile(
loss="binary_crossentropy",
metrics=["acc"],
)

# 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
model.fit(
train_dataset,
validation_data=valid_dataset,
epochs=epochs,
shuffle=True,
verbose=2,
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:

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

• link you provide to the notebook is not working. Oct 12, 2021 at 9:34