Medical Imaging Classification

Is Frozen Weight Transfer Learning Always the Answer?

This repo's main focus isn't on data processing or creating image classification models like Inception, Xception, VGG16, etc. However, it is about how you may improve your accuracy by changing just one parameter. Naturally, instructions and code for training a classification model will be given.

Dataset used

The dataset used for this task was Kvasir, it is available in Kaggle, if you want to learn more about it then you can check this link.

Important part of the repo

In the function return_model():

def return_model(input_dim, nb_classes, freeze=False, head=None):
    if head == 'xception' or head == 'Xception':
        base_model = Xception(include_top=False, weights='imagenet', input_shape=(input_dim, input_dim, 3))
    
    if head == 'vgg16' or head == 'VGG16':
        base_model = VGG16(include_top=False, weights='imagenet', input_shape=(input_dim, input_dim, 3))
    
    if head == 'inceptionv3' or 'Inceptionv3' or 'InceptionV3':
        base_model = InceptionV3(include_top=False, weights='imagenet', input_shape=(input_dim, input_dim, 3))
    
    if head == 'densenet121':
        base_model = DenseNet121(include_top=False, weights='imagenet', input_shape=(input_dim, input_dim, 3))
    
    if not head:
        print('Please choose the pretrained model')

    print('Chosen model is:', head)

    if freeze:
        for layer in base_model.layers:
            layer.trainable = False


    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    # x = Dense(1024, activation='sigmoid')(x)

    predictions = Dense(nb_classes, activation='softmax')(x)
    model = Model(inputs=base_model.inputs, outputs=predictions)

    return model

The pre-trained model's weights should not be frozen because doing so increases the model's capacity for learning.

Run the inference

I added a script video_classification.py that helps you do the inference in a video using one of your models. And I added also a script predict_options.py that contains multiple inference options such as:

• Predict one image
• Predict one array
• Predict a folder
• Predict a video
• Predict from the webcam

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