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Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification

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Skin Lesions Classification with Deep Convolutional Neural Network

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!

Files Description

  • 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

Technical Issue

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

Results

Models Validation Test Depth # Params
Baseline 77.48% 76.54% 11 layers 2,124,839
Fine-tuned VGG16 (from last block) 79.82% 79.64% 23 layers 14,980,935
Fine-tuned Inception V3 (from the last 2 inception blocks) 79.935% 79.94% 315 layers 22,855,463
Fine-tuned Inception-ResNet V2 (from the Inception-ResNet-C) 80.82% 82.53% 784 layers 55,127,271
Fine-tuned DenseNet 201 (from the last dense block) 85.8% 83.9% 711 layers 19,309,127
Fine-tuned Inception V3 (all layers) 86.92% 86.826% _ _
Fine-tuned DenseNet 201 (all layers) 86.696% 87.725% _ _
Ensemble of fully-fine-tuned Inception V3 and DenseNet 201 88.8% 88.52% _ _

The Dataset

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