# Skin Disease Classification versus Skin Lesion Characterization: Achieving Robust Diagnosis using Multi-label Deep Neural Networks

## TL;DR

This paper compares disease-targeted and lesion-targeted CNN classifiers for skin diagnosis, finding that lesion classification yields higher accuracy and is more visually consistent, thus supporting more robust diagnosis strategies.

## Contribution

The study demonstrates that focusing on skin lesion characteristics with CNNs improves classification accuracy and robustness over direct disease diagnosis methods.

## Key findings

- Lesion-targeted classification achieves higher mean average precision (0.70) than disease-targeted classification (0.42).
- Lesion classification provides more visually consistent features for diagnosis.
- Using lesion tags as targets enhances the robustness of skin disease diagnosis.

## Abstract

In this study, we investigate what a practically useful approach is in order to achieve robust skin disease diagnosis. A direct approach is to target the ground truth diagnosis labels, while an alternative approach instead focuses on determining skin lesion characteristics that are more visually consistent and discernible. We argue that, for computer-aided skin disease diagnosis, it is both more realistic and more useful that lesion type tags should be considered as the target of an automated diagnosis system such that the system can first achieve a high accuracy in describing skin lesions, and in turn facilitate disease diagnosis using lesion characteristics in conjunction with other evidence. To further meet such an objective, we employ convolutional neural networks (CNNs) for both the disease-targeted and lesion-targeted classifications. We have collected a large-scale and diverse dataset of 75,665 skin disease images from six publicly available dermatology atlantes. Then we train and compare both disease-targeted and lesion-targeted classifiers, respectively. For disease-targeted classification, only 27.6% top-1 accuracy and 57.9% top-5 accuracy are achieved with a mean average precision (mAP) of 0.42. In contrast, for lesion-targeted classification, we can achieve a much higher mAP of 0.70.