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Class Imbalanced Dermoscopy with GANs by barbara liuva chia policarpo is a document available to read on EtoBox.

What is Class Imbalanced Dermoscopy with GANs about?

This paper addresses the challenge of class imbalance in dermoscopic image classification for skin cancer detection by utilizing data augmentation and generative adversarial networks (GAN). The authors investigate the performance of fine-tuned deep learning models on imbalanced datasets and compare the effectiveness of data augmentation versus GAN-generated synthetic images. The study aims to enhance classification accuracy and provide insights into the best methods for tackling class imbalances in dermosco

Author

barbara liuva chia policarpo

Language

EN