Quantitative Analysis of Skin using Diffuse Reflectance for Non-invasive Pigments Detection

Skin diagnosis has become a significant part of research topics in biomedical engineering and informatics, since many conditions or symptoms of diseases, such as melanoma and jaundice, are indicated by skin appearance. In the past, an invasive method (i.e. Biopsy) is widely used for pathological diagnosis by removing a small amount of living tissue. Recently, non-invasive methods have been studied based on diffuse reflectance for detecting skin inner information. With the development of machine learning techniques, non-invasive methods can be further improved in many aspects, such as the speed and accuracy. Our research focuses on analyzing and improving non-invasive skin pigments detection using neural networks. The relation between skin pigments content and skin diffuse reflectance has been studied. Moreover, the computational time has been accelerated significantly after using the inverse mapping neural network instead of the forward mapping one. The results show that our proposed method can obtain favorable results in estimating melanin content, blood content, and oxygen saturation from synthetic skin diffuse reflectance for all lightly, moderately, and darkly pigmented skin types compared to Monte Carlo simulations. And it turns out that our method works well when using a measured skin reflectance database from National Institute of Standards and Technology for the second validation.

Automated summary

Skin diagnosis, particularly concerning conditions like melanoma and jaundice, has shifted towards non-invasive methods such as diffuse reflectance for detecting inner skin information. With the advancement of machine learning techniques, non-invasive methods like neural networks have shown improved accuracy and speed in analyzing skin pigments. The use of inverse mapping neural networks has significantly accelerated computational time.