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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A. Singal and R. Arora, “Nail as a window of systemic diseases,” Indian Dermatology Online Journal, vol. 6, no. 2, pp. 67, 2015, doi: 10.4103/2229-5178.153002.

R. Theunuo, S. S. K. Ajithkumar, B. George, and R. S. Salim, “Nail Changes in Leprosy: Onychoscopy Evaluation,” Indian Dermatology Online Journal, vol. 11, no. 6, pp. 971–974, 2020, doi: 10.4103/idoj.IDOJ.

A. Polat and Y. Kapıcıoğlu, “Dermoscopic findings of psoriatic nail and their relationship with disease severity,” Turkderm, vol. 51, no. 4, pp. 119–123, 2017, doi: 10.4274/turkderm.54289.

U. Wollina, P. Nenoff, G. Haroske, and H. A. Haenssle, “The diagnosis and treatment of nail disorders,” Dtsch. Arztebl. Int., vol. 113, no. 29–30, pp. 509–517, 2016, doi: 10.3238/arztebl.2016.0509.

J. K. Reinecke and M. A. Hinshaw, “Nail health in women,” International Journal Women’s Dermatology, vol. 6, no. 2, pp. 73–79, 2020, doi: 10.1016/j.ijwd.2020.01.006.

T. S. Indi and Y. A. Gunge, “Early Stage Disease Diagnosis System Using Human Nail Image Processing,” International Journal of Information Technology and Computer Science, vol. 8, no. 7, pp. 30–35, 2016, doi: 10.5815/ijitcs.2016.07.05.

E. Haneke, “Nail psoriasis: clinical features, pathogenesis, differential diagno-ses, and management,” Psoriasis Targets Ther., vol. Volume 7, pp. 51–63, 2017, doi: 10.2147/ptt.s126281.

D. K. Lee and S. R. Lipner, “Optimal diagnosis and management of common nail disorders,” Annals of Medicine, vol. 54, no. 1, pp. 694–712, 2022, doi: 10.1080/07853890.2022.2044511.

S. H. Lee and C. S. Yang, “Fingernail analysis management system using mi-croscopy sensor and blockchain technology,”International Journal of Distributed Sensor Networks, vol. 14, no. 3, 2018, doi: 10.1177/1550147718767044.

S. Easwaramoorthy, F. Sophia, and A. Prathik, “Biometric authentication using finger nails,” in 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 - Proceedings, 2016, pp. 1–6, doi: 10.1109/ICETETS.2016.7603054.

T. S. Indi and D. D. Patil, “Nail Feature Analysis and Classification Techniques for Disease Detection,” International Journal of Computer Sciences and Engineering, vol. 7, no. 5, pp. 1376–1383, 2019, doi: 10.26438/ijcse/v7i5.13761383.

P. Maniyan and B. L. Shivakumar, “Detection of Diseases using Nail Image Processing Based on Multiclass SVM Classifier Method,” International Journal of Engineering Science and Computing, vol. 8, no. 5, pp. 17382–17390, 2018.

D. H. S. H. Dr.S.Suguna, K.Hemanandhini, “Detection of Nail Peculiarities Using Nail Image Processing Techniques,” Science, Technology and Development, vol. IX, no. Xi, pp. 174–188, 2020.

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, no. 3, pp. 685–695, 2021, doi: 10.1007/s12525-021-00475-2.

Reubenindustrustech, “Nail disease image augmentation,”Kaggle, 2021.

Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma, and B. Yu, “Recent advances in convolutional neural network acceleration,”Neurocomputing, vol. 323, pp. 37–51, 2019, doi: 10.1016/j.neucom.2018.09.038.

M. S. Abou El-Seoud, M. H. Siala, and G. McKee, “Detection and Classifica-tion of White Blood Cells Through Deep Learning Techniques,” International Journal of online and Biomedical Engineering, vol. 16, no. 15, pp. 94–105, 2020, doi: 10.3991/ijoe.v16i15.15481.

L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021.

D. Bendarkar, P. Somase, P. Rebari, R. Paturkar, and A. Khan, “Web Based Recognition and Translation of American Sign Language with CNN and RNN,” International Journal of online and Biomedical Engineering, vol. 17, no. 1, pp. 34–50, 2021, doi: 10.3991/ijoe.v17i01.18585.

A. Elnakib, H. M. Amer, and F. E. Z. Abou-Chadi, “Early lung cancer detection using deep learning optimization,” International Journal of online and Biomedical Engineering, vol. 16, no. 6, pp. 82–94, 2020, doi: 10.3991/ijoe.v16i06.13657.

Y. T. Li and J. I. Guo, “A VGG-16 based Faster RCNN Model for PCB Error Inspection in Industrial AOI Applications,” in IEEE International Conference on Consumer Electronics-Taiwan, 2018, pp. 1–2.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015, pp. 1–14.

B. R. Nanditha, A. G. Kiran, H. S. Chandrashekar, M. S. Dinesh, and S. Murali, “An Ensemble Deep Neural Network Approach for Oral Cancer Screening,” International Journal of online and Biomedical Engineering, vol. 17, no. 2, pp. 121–134, 2021, doi: 10.3991/ijoe.v17i02.19207.

J. Abdulhadi, A. Al-Dujaili, A. J. Humaidi, and M. A. R. Fadhel, “Human nail diseases classification based on transfer learning,” ICIC Express Letters, vol. 15, no. 12, pp. 1271–1282, 2021, doi: 10.24507/icicel.15.12.1271.

R. Nijhawan, R. Verma, Ayushi, S. Bhushan, R. Dua, and A. Mittal, “An integrated deep learning framework approach for nail disease identification,” in Proceedings - 13th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2017, 2018, vol. 2018-Jan., pp. 197–202, doi: 10.1109/SITIS.2017.42


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