Skin Lesions Classification DCNNs Save
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification
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
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Final report: Project_Report.pdf
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Exploratory data analysis: Skin_Cancer_EDA.ipynb
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Baseline model: Baseline_CNN.ipynb
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Fine-tuning the last convolutional block of VGG16: Fine_Tuning_VGG16.ipynb
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Fine-tuning the top 2 inception blocks of InceptionV3: Fine_Tuning_InceptionV3.ipynb
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Fine-tuning the Inception-ResNet-C of Inception-ResNet V2: Fine_Tuning_InceptionResNet.ipynb
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Fine-tuning the last dense block of DenseNet 201: Fine_Tuning_DenseNet.ipynb
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Fine-tuning all layers of pretrained Inception V3 on ImageNet: Retraining_InceptionV3.ipynb
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Fine-tuning all layers of pretrained DenseNet 201 on ImageNet: Retraining_DenseNet.ipynb
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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
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