Deep neural network ensemble by data augmentation and bagging for skin lesion classification

15 Jul 2018 Manik Goyal Jagath C. Rajapakse

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... (read more)

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Methods used in the Paper


METHOD TYPE
ReLU
Activation Functions
Residual Connection
Skip Connections
Inception-ResNet-v2 Reduction-B
Image Model Blocks
Inception-ResNet-v2-A
Image Model Blocks
Inception-ResNet-v2-B
Image Model Blocks
Inception-ResNet-v2-C
Image Model Blocks
Inception-ResNet-v2
Convolutional Neural Networks
Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Inception-C
Image Model Blocks
Inception-B
Image Model Blocks
Max Pooling
Pooling Operations
Softmax
Output Functions
Convolution
Convolutions
Dropout
Regularization
Inception-A
Image Model Blocks
Reduction-A
Image Model Blocks
Reduction-B
Image Model Blocks
Inception-v4
Convolutional Neural Networks