# Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation

## TL;DR

This paper develops deep learning tools for data purification and augmentation to improve melanoma detection, addressing database limitations like small size, imbalance, and occlusions, and demonstrates improved detection performance.

## Contribution

Introduces novel deep learning tools for data purification and augmentation to enhance melanoma detection accuracy in limited and imbalanced datasets.

## Key findings

- Improved melanoma detection performance with data purification and augmentation.
- Effective removal of image occlusions using the processing unit.
- Generation of virtual lesion images with GANs enhances class balance.

## Abstract

Melanoma is one of the ten most common cancers in the US. Early detection is crucial for survival, but often the cancer is diagnosed in the fatal stage. Deep learning has the potential to improve cancer detection rates, but its applicability to melanoma detection is compromised by the limitations of the available skin lesion databases, which are small, heavily imbalanced, and contain images with occlusions. We build deep-learning-based tools for data purification and augmentation to counter-act these limitations. The developed tools can be utilized in a deep learning system for lesion classification and we show how to build such a system. The system heavily relies on the processing unit for removing image occlusions and the data generation unit, based on generative adversarial networks, for populating scarce lesion classes, or equivalently creating virtual patients with pre-defined types of lesions. We empirically verify our approach and show that incorporating these two units into melanoma detection system results in the superior performance over common baselines.