Classification of Koilonychia, Beaus Lines, and Leukonychia based on Nail Image using Transfer Learning VGG-16

Sugondo Hadiyoso, Suci Aulia


Human nail disease is usually ignored since it does not reveal clinical signs that are harmful to one's health. Nail disease, on the other hand, can be an early sign of a health issue. Some types of nail disease can cause infection, injury, or even the loss of the nail itself. It can reduce a person's aesthetics and beauty. Nail disease is very varied, so it is often difficult for clinicians to diagnose because several types have high similarities. Therefore, an automatic nail disease classification method based on nail photos was proposed in this study. The proposed method was based on the VGG-16 neural network architecture with an Adam optimizer. Nail diseases including Koilonychia, Beaus Lines, Leukonychia have been classified in this study. The model in this study is simulated in Python programming. The simulation results show that the highest classification accuracy is 96%, achieved with epoch-10. The transfer learning method based on a neural network simulated in this study is expected to support the clinical diagnosis of nail disease.


nail disease; neural network; transfer learning; VGG-16

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