Deep-Learning-Based Hair Damage Diagnosis Method Applying Scanning Electron Microscopy Images

In recent years, with the gradual development of medicine and deep learning, many technologies have been developed. In the field of beauty services or medicine, it is particularly important to judge the degree of hair damage. Because people in modern society pay more attention to their own dressing and makeup, changes in the shape of their hair have become more frequent, e.g., owing to a perm or dyeing. Thus, the hair is severely damaged through this process. Because hair is relatively thin, a quick determination of the degree of damage has also become a major problem. Currently, there are three specific methods for this purpose. In the first method, professionals engaged in the beauty service industry make a direct judgement with the naked eye. The second way is to observe the damaged cuticle layers of the hair using a microscope, and then make a judgment. The third approach is to conduct experimental tests using physical and chemical methods. However, all of these methods entail certain limitations, inconveniences, and a high complexity and time consumption. Therefore, our proposed method is to use scanning electron microscope to collect various hair sample images, combined with deep learning to identify and judge the degree of hair damage. This method will be used for hair quality diagnosis. Experiment on the data set we made, compared with the experimental results of other lightweight networks, our method showed the highest accuracy rate of 94.8%.

Automated summary

With the development of medicine and deep learning technologies, judging the degree of hair damage has become more important. There are currently three methods available, but all have certain limitations and inconveniences, leading to the proposal of a method combining scanning electron microscope and deep learning for improved accuracy.