The Advanced Digital Acne Type Diagnostic System

This paper introduces the Advanced Digital Acne Type Diagnostic Strategy, a novel approach leveraging advanced technologies for enhanced acne vulgaris diagnosis and classification. The system integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Support Vector Machines (SVMs) to accurately classify five acne types-comedones, cysts, nodules, papules, and pustules-from user-uploaded images. It further incorporates instance segmentation and object detection models for comprehensive acne severity assessment, guiding tailored treatment recommendations based on severity and acne type. Moreover, the study pioneers synthetic data generation using Generative Adversarial Networks (GANs) and stable diffusion models to create a diverse dataset of acne images, addressing the scarcity and diversity limitations of existing clinical datasets. Results demonstrate the system's efficacy in improving early acne detection, facilitating timely interventions, and enhancing patient outcomes through accessible, real-time digital diagnostics.