This is a 40-hour project for CIS 5526 Machine Learning. For full description and analysis please refer to Project_Report.pdf.

Future work in better training strategy and exploring other models such as Xception and creating a bigger ensemble can help the model performs better than this!

• Final report: Project_Report.pdf

• Exploratory data analysis: Skin_Cancer_EDA.ipynb

• Baseline model: Baseline_CNN.ipynb

• Fine-tuning the last convolutional block of VGG16: Fine_Tuning_VGG16.ipynb

• Fine-tuning the top 2 inception blocks of InceptionV3: Fine_Tuning_InceptionV3.ipynb

• Fine-tuning the Inception-ResNet-C of Inception-ResNet V2: Fine_Tuning_InceptionResNet.ipynb

• Fine-tuning the last dense block of DenseNet 201: Fine_Tuning_DenseNet.ipynb

• Fine-tuning all layers of pretrained Inception V3 on ImageNet: Retraining_InceptionV3.ipynb

• Fine-tuning all layers of pretrained DenseNet 201 on ImageNet: Retraining_DenseNet.ipynb

• Ensemble model of the fully fine-tuned Inception V3 and DenseNet 201 (best result): Ensemble_Models.ipynb

I'm using Keras 2.2.4 and Tensorflow 1.11. Batch-Norm layer in this version of Keras is implemented in a way that: during training your network will always use the mini-batch statistics either the BN layer is frozen or not; also during inference you will use the previously learned statistics of the frozen BN layers. As a result, if you fine-tune the top layers, their weights will be adjusted to the mean/variance of the new dataset. Nevertheless, during inference they will receive data which are scaled differently because the mean/variance of the original dataset will be used. Consequently, if use Keras's example codes for fine-tuning Inception V3 or any network with batch norm layer, the results will be very bad. Please refer to issue #9965 and #9214. One temporary solution is:

for layer in pre_trained_model.layers:
    if hasattr(layer, 'moving_mean') and hasattr(layer, 'moving_variance'):
        layer.trainable = True
        K.eval(K.update(layer.moving_mean, K.zeros_like(layer.moving_mean)))
        K.eval(K.update(layer.moving_variance, K.zeros_like(layer.moving_variance)))
    else:
        layer.trainable = False

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions