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

arXiv: https://arxiv.org/abs/1807.05496 · HTML: https://mlsanity.com/paper/1807.05496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Data-Driven Deep Supervision for Skin Lesion Classification](https://mlsanity.com/paper/2209.01527/) (2022)

## Abstract (arXiv)

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 1×1 convolution for fusion in a meta-learning layer.