Cancer-Net SCa-Synth: An Open Access Synthetically Generated 2D Skin Lesion Dataset for Skin Cancer Classification

TL;DR

This paper presents Cancer-Net SCa-Synth, a large synthetic dataset of skin lesions generated using advanced AI models to improve skin cancer classification accuracy and address data imbalance issues.

Contribution

We introduce a new synthetic skin lesion dataset using Stable Diffusion and DreamBooth, enhancing deep learning models for skin cancer detection.

Findings

Synthetic data improves model performance on ISIC 2020 test set

The dataset is publicly available for research use

Synthetic images help balance class distribution in datasets

Abstract

In the United States, skin cancer ranks as the most commonly diagnosed cancer, presenting a significant public health issue due to its high rates of occurrence and the risk of serious complications if not caught early. Recent advancements in dataset curation and deep learning have shown promise in quick and accurate detection of skin cancer. However, current open-source datasets have significant class imbalances which impedes the effectiveness of these deep learning models. In healthcare, generative artificial intelligence (AI) models have been employed to create synthetic data, addressing data imbalance in datasets by augmenting underrepresented classes and enhancing the overall quality and performance of machine learning models. In this paper, we build on top of previous work by leveraging new advancements in generative AI, notably Stable Diffusion and DreamBooth. We introduce Cancer-Net SCa-Synth, an open access synthetically generated 2D skin lesion dataset for skin cancer classification. Further analysis on the data effectiveness by comparing the ISIC 2020 test set performance for training with and without these synthetic images for a simple model highlights the benefits of leveraging synthetic data to improve performance. Cancer-Net SCa-Synth is publicly available at https://github.com/catai9/Cancer-Net-SCa-Synth as part of a global open-source initiative for accelerating machine learning for cancer care.