Assessing a Proposed Dynamic Ratio in Dataset Class Imbalance with GAN's Generated Melanoma Images

Skin cancer cases has increased in the last years and melanoma cases have a high rank in the incidence of mortality. However class imbalance in melanoma dataset produces a challenge for researcher to tackle a classification model. This work proposes the use of a dynamic ratio in dataset imbalance with the addition of synthetic image generation by Progressive Growth of Generative Adversarial Networks (PGGAN) and the assessment of this ratio problem. Our methodology involved the preprocessing of the ISIC 2019 challenge dataset, selection between PGGAN and Wasserstein GAN with Gradient Penalty (WGAN-GP) for image generation, dynamic ratio addition in original dataset, use of Resnet 50 pretrained model and evaluation of classification metrics. Our study demonstrated selected ratios that improved precision, recall and F1-score metrics on melanoma detection against original dataset with traditional image transformations.