Deep neural network ensemble by data augmentation and bagging for skin lesion classification
This work summarizes our submission for the Task 3: Disease Classification of ISIC 2018 challenge in Skin Lesion Analysis Towards Melanoma Detection. We use a novel deep neural network (DNN) ensemble architecture introduced by us that can effectively classify skin lesions by using data-augmentation and bagging to address paucity of data and prevent over-fitting. The ensemble is composed of two DNN architectures: Inception-v4 and Inception-Resnet-v2. The DNN architectures are combined in to an ensemble by using a $1\times1$ convolution for fusion in a meta-learning layer.
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Methods
1x1 Convolution •
Average Pooling •
Convolution •
Dropout •
Inception-A •
Inception-B •
Inception-C •
Inception-ResNet-v2 •
Inception-ResNet-v2-A •
Inception-ResNet-v2-B •
Inception-ResNet-v2-C •
Inception-ResNet-v2 Reduction-B •
Inception-v4 •
Max Pooling •
Reduction-A •
Reduction-B •
ReLU •
Residual Connection •
Softmax
