Skin Lesions Classification with Deep Convolutional Neural Network

Dataset

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

Files Tables

Sr.No	File Name	Link
1	Exploratory data analysis	Notebook
2	Baseline model	Notebook
3	Fine-tuning the last convolutional block of VGG16	Notebook
4	Fine-tuning the top 2 inception blocks of InceptionV3	Notebook
5	Fine-tuning the Inception-ResNet-C of Inception-ResNet V2	Notebook
6	Fine-tuning the last dense block of DenseNet 201	Notebook
7	Fine-tuning all layers of pretrained Inception V3 on ImageNet	Notebook
8	Fine-tuning all layers of pretrained DenseNet 201 on ImageNet	Notebook
9	Ensemble model of the fully fine-tuned Inception V3 and DenseNet 201	Notebook

Technical Issue

All Notebooks 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