Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network

Abstract

With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair is being widely used worldwide, and the chemical and physical treatment of hair is also increasing. As a result, hair has suffered a lot of damage, and the degree of damage to hair has been measured only by the naked eye or touch. This has led to serious consequences, such as hair damage and scalp diseases. However, although these problems are serious, there is little research on hair damage. With the advancement of technology, people began to be interested in preventing and reversing hair damage. Manual observation methods cannot accurately and quickly identify hair damage areas. In recent years, with the rise of artificial intelligence technology, a large number of applications in various scenarios have given researchers new methods. In the project, we created a new hair damage data set based on SEM (scanning electron microscope) images. Through various physical and chemical analyses, we observe the changes in the hair surface according to the degree of hair damage, found the relationship between them, used a convolutional neural network to recognize and confirm the degree of hair damage, and categorized the degree of damage into weak damage, moderate damage and high damage.

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

“…In this research, the authors propose a novel hair loss detection network. Damage to hair surfaces seen under a scanning electron microscope (SEM) was analyzed using AI techniques, which automatically detected and categorized the damage 28 . Simultaneously, they used SEM microscopy pictures to create a new data set on hair thinning.…”

“…Damage to hair surfaces seen under a scanning electron microscope (SEM) was analyzed using AI techniques, which automatically detected and categorized the damage. 28 Simultaneously, they used SEM microscopy pictures to create a new data set on hair thinning. Damage to the hair was classified into mild, moderate, and severe categories using mathematical analysis of SEM (scanning electron microscopy) image data to create a new hair microscope data set.…”

Effects of Cosmetic Emulsions on the Surface Properties of Mongolian Hair

“…The effects of different types of damage and subsequent “repair” by conditioner formulations on the morphological, structural, and physicochemical properties of human hair have been studied extensively in the past, using a wide range of sophisticated characterization techniques such as optical microscopy, atomic force microscopy, X-ray photoelectron spectroscopy or wetting experiments. − In the present work, we built upon this knowledge and investigated the overall effect of oxidative bleaching on hair surfaces and the (partial) recovery of native properties by treatment with cosmetic emulsions, highlighting three distinct aspects: first, all our studies were focused on Mongolian hair, a subclass of the Asian type, which has received much less attention in past research in comparison to the Caucasian type despite its high relevance for the global personal care market. Although human hair is commonly divided into three major categories (Asian, African, and Caucasian), such broad classifications fail to adequately describe the extreme biological diversity of hair types found in local ethnic groups of different or even mixed origin. − Asian hair is generally known for its dark color, straight to wavy curvature, and large diameter, which together with compact cuticle structures render this type of hair mechanically very robust .…”