# Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network
## 1 Introduction  
Hair is an important part of human body image. Modern people's aesthetic desires for beauty, along with the continuous growth and diversification of hair and the continuous development of hair shape, color, and texture allows people to use their own personality to change their image to follow the ever-changing trends. Hair is composed of 1-8% external hydrophobic lipid epidermis, 80-90% \(\alpha\)-helix or \(\beta\)-sheet conformation of parallel polypeptide chains to form water-insoluble keratin, less than 3% melanin pigment, and 0.6-1.0% trace elements, 10-15% moisture, etc. The normal cuticle has a smooth appearance, reflecting light and limiting friction between hair shafts. It is responsible for the shine and texture of hair. The keratin layer of hair becomes fragile and cracked under the influence of the external environment, temperature, humidity, along with chemical and physical treatments, thus affecting hair quality. Although most people's hair is prone to various damage problems because hair is inconvenient to observe, it is impossible to conduct a detailed analysis, and there are few studies related to hair damage.  
With the widespread application of photomicrography in the field of hair microstructure analysis, it has become easier to observe hair details that were difficult to clearly observe with traditional microscopes. Clinically, microscopic analysis can be used as a tool to assess hair damage, as an indicator of health status, by identifying the morphological characteristics of hair damage, and it can be analyzed in a qualitative manner. Hair's exposure to various chemical agents and physical effects, such as detergents, dyes, combing, ultraviolet rays, etc., will affect hair. Researchers have conducted related studies accordingly. However, there is limited research that quantifies the degree of hair damage based on the morphological characteristics of hair. Traditionally, it is difficult to distinguishhair damage in detail using only manual observation with an optical microscope. With the development of microscopy techniques and the application of scanning electron microscopy (SEM) in multiple fields, the microscopic details of hair are easier to find. For example, [PERSON] et al. [1] used scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and other physical and chemical methods to identify the effects of alopecia areata on the structure and composition of human hair. [PERSON] et al. [2] used a heating iron to heat hair and observed hair images under a scanning electron microscope to evaluate the damage to the epidermis and cortex of the hair fiber by heat loss. Although these manual methods use scanning electron microscope imaging technology to analyze hair microscopic images, they do not perform detailed quantitative classification of hair damage.  
In this paper, we propose a novel hair damage detection network. Based on artificial intelligence algorithms, were used to analyze scanning electron microscope (SEM) hair surface image damage (Figure 1) to automatically identify and categorized the damage. At the same time, we established a new hair damage data set based on SEM microscopic image data. To summarize, the contributions of this work are as follows:  
* We created a new hair microscopy data set based on SEM (Scanning Electron Microscope) image data and performed a quantitative analysis to classify the degree of hair damage under the categories: weak damage, moderate damage, and high damage.
* We proposed a novel and effective convolutional network model for hair damage detection: RCSAN-Net (residual channel spatial attention network).
* We designed and introduced a channel and spatial attention mechanism into the hair damage detection model to gather hair features to improve the accuracy of detection and recognition.