# Skin lesion detection based on an ensemble of deep convolutional neural network

**Authors:** Balazs Harangi  
**arXiv:** [1705.03360](https://arxiv.org/abs/1705.03360)  
**Date:** 2017-05-09  
**Summary page:** https://www.summarizepaper.com/en/paper/1705.03360v1/

## Key points

- Skin cancer is a major public health concern in the United States with over 5 million new cases diagnosed annually.
- Melanoma, the deadliest form of skin cancer, accounts for 9,000 deaths each year.
- Balazs Harangi proposed an ensemble of deep convolutional neural networks to classify dermoscopy images into three classes.
- The aim was to achieve the highest possible classification accuracy by combining the outputs from four different neural architectures.
- This fusion-based approach used confidence values provided by their final softmax layers to weight individual accuracies and outperform all individual neural networks in terms of classification accuracy.
- This promising solution could improve early detection and treatment outcomes for skin cancer patients.

## Summary

Skin cancer is a major public health concern in the United States with over 5 million new cases diagnosed annually. Of these cases, melanoma - the deadliest form of skin cancer - accounts for 9,000 deaths each year. To address this issue, Balazs Harangi proposed an ensemble of deep convolutional neural networks to classify dermoscopy images into three classes. The aim was to achieve the highest possible classification accuracy by combining the outputs from four different neural architectures. This fusion-based approach used confidence values provided by their final softmax layers to weight individual accuracies and outperform all individual neural networks in terms of classification accuracy. This promising solution could improve early detection and treatment outcomes for skin cancer patients.