Acne Detection Based on Reconstructed Hyperspectral Images

Abstract

Acne Vulgaris is a common type of skin disease that affects more than 85% of teenagers and frequently continues even in adulthood. While it is not a dangerous skin disease, it can significantly impact the quality of life. Hyperspectral imaging (HSI), which captures a wide spectrum of light, has emerged as a tool for the detection and diagnosis of various skin conditions. However, due to the high cost of specialised HS cameras, it is limited in its use in clinical settings. In this research, a novel acne detection system that will utilise reconstructed hyperspectral (HS) images from RGB images is proposed. A dataset of reconstructed HS images is created using the best-performing HS reconstruction model from our previous research. A new acne detection algorithm that is based on reconstructed HS images and RetinaNet algorithm is introduced. The results indicate that the proposed algorithm surpasses other techniques based on RGB images. Additionally, reconstructed HS images offer a promising and cost-effective alternative to using expensive HSI equipment for detecting conditions like acne or other medical issues.

Keywords

• acne detection
• hyperspectral imaging
• hyperspectral reconstruction
• RetinaNet
• machine learning