RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Abstract

Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy–Paste

“…This proposed method performs semi-supervised learning while maintaining a bidirectional relationship between the two sets of data. Additionally, to overcome the lack of training data, it was proposed to use generative models like StyleGAN2 [18,19] to create images for use in acne training [20,21]. We conducted semi-supervised learning of the acne segmentation model using BCP.…”