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Derin Öğrenme ile Cilt Tipi Tespiti: Karşılaştırmalı Bir Analiz

Year 2023, Volume: 11 Issue: 2, 729 - 742, 30.04.2023
https://doi.org/10.29130/dubited.930096

Abstract

Yaş ve çevre dahil görünümü değiştirebilecek ve etkileyebilecek birçok faktör vardır. Cilt tipini bilmek, cildin ihtiyaçlarına en uygun ürünleri ve dolayısıyla doğru cilt bakımını seçmeye yardımcı olur. Son zamanlarda, kozmetik için artan talep ve yeterli donanıma sahip kozmetikçilerin azlığı, kozmetoloji merkezlerini yapay zekâ uygulamaları kullanarak ihtiyacı karşılamaya teşvik etmiştir. Derin öğrenme uygulamaları, cilt tipinin belirlenmesinde yüksek doğrulukta sonuçlar verebilir. Son araştırmalar gösteriyor ki, öğrenme, doğrusal olmayan veriler üzerinde makine öğrenimi yöntemlerinden daha iyi sonuçlar verir. Bu çalışmanın amacı, derin öğrenme ile cilt analizi verilerinde cilt tipi tahmini için en iyi sınıflandırma modelini bulmaktır. Bu amaç doğrultusunda, Sgd, Adagrad, Adam ve Adamax olmak üzere 4 farklı optimizasyon algortiması; Tanh ve ReLU aktivasyon fonksiyonlarını ve farklı nöron sayılarının kombinasyonları ile 16 farklı model oluşturulmuştur. Deneysel çalışmalarda, modellerin performansı parametrelere göre değişmekte olup en başarılı derin sinir ağı modelinin 93,75 başarı oranı ile 64 nöron, Sgd optimizasyon fonksiyonu ve ReLU aktivasyon fonksiyonu kombinasyonundan oluşan modelin olduğu gözlemlenmiştir. Elde edilen doğruluk sonucu diğer yöntemlere kıyasla daha yüksek bir sınıflandırma başarısına sahiptir ve derin sinir ağlarının doğru bir şekilde cilt tipi sınıflandırması yapabileceğini göstermektedir.

References

  • Seda S.(2019) Kozmetolojik Cilt Analizi http://www.sedasakaci.com/tr/ScientificAnalysis Access Date: 12.04.2021
  • Ashqar, B. A., Abu-Nasser, B. S., & Abu-Naser, S. S. (2019). Plant Seedlings Classification Using Deep Learning.
  • Alarifi J.S., Goyal M., Davison A.K., Dancey D., Khan R., Yap M.H. (2017) Facial Skin Classification Using Convolutional Neural Networks. In: Karray F., Campilho A., Cheriet F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science, vol 10317. Springer, Cham
  • Sun Gyoo Park, Young Deuk Kim, Jin Jun Kim, Seh Hoon Kang, Two possible classifications of facial skin type by two parameters in Korean women: sebum excretion rate (SER) and skin surface relief (SSR), First published: 27 October 2006, https://doi.org/10.1111/j.1600-0846.1999.tb00130.x , Skin Research and Technology
  • Hiroko Kumagai, Kazumi Shioya, Kiyoshi Kawasaki, Izumi Horii, Junichi Koyara, Yasuhisa Nakayama, Wataru Mori, Saburo Ohta, Development of a Scientific Method for Classification of Facial Skin Types, Journal of Society of Cosmetic Chemists of Japan, 1985, Volume 19, Issue 1, Pages 9-19
  • Li, Y., Nie, X., & Huang, R., Web spam classification method based on deep belief networks, Expert Systems with Applications, 96, 261-270, 2018.
  • Bengio, Y., Learning deep architectures for AI. Foundations and trends in Machine Learning, 2 (1), 1- 127, 2009.
  • Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., & De Vos, M. (2018). Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering, 66(5), 1285-1296.
  • Li, D., Huang, F., Yan, L., Cao, Z., Chen, J., & Ye, Z. (2019). Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: Comparisons with multilayer-perceptron-only, bp neural network, and information value models. Applied Sciences, 9(18), 3664.
  • Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., & Mrzljak, V. (2020). Modeling the spread of COVID-19 infection using a multilayer perceptron. Computational and mathematical methods in medicine, 2020.
  • Arı, A., & Berberler, M. E. (2017). Yapay sinir ağları ile tahmin ve sınıflandırma problemlerinin çözümü için arayüz tasarımı. Acta Infologica, 1(2), 55-73.
  • A. V. Savchenko, "Probabilistic Neural Network With Complex Exponential Activation Functions in Image Recognition," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 651-660, Feb. 2020, doi: 10.1109/TNNLS.2019.2908973.
  • E. M. Dogo, O. J. Afolabi, N. I. Nwulu, B. Twala and C. O. Aigbavboa, "A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks," 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Belgaum, India, 2018, pp. 92-99, doi: 10.1109/CTEMS.2018.8769211.
  • Quang, D., Guan, Y., & Parker, S. C. (2018). YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU. Bioinformatics, 34(20), 3578-3580.
  • Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and libraries for deep learning. Journal of digital imaging, 30(4), 400-405.
  • Parvat, A., Chavan, J., Kadam, S., Dev, S., & Pathak, V. (2017, January). A survey of deep-learning frameworks. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1-7). IEEE.
  • Wang, Z., Yan, M., Chen, J., Liu, S., & Zhang, D. (2020, November). Deep learning library testing via effective model generation. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 788-799).
  • Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and libraries for deep learning. Journal of digital imaging, 30(4), 400-405.
  • Chung, Y., Ahn, S., Yang, J., & Lee, J. (2017). Comparison of deep learning frameworks: about theano, tensorflow, and cognitive toolkit. Journal of Intelligence and Information Systems, 23(2), 1-17.

Skin Type Detection with Deep Learning: A Comparative Analysis

Year 2023, Volume: 11 Issue: 2, 729 - 742, 30.04.2023
https://doi.org/10.29130/dubited.930096

Abstract

There are many factors that can change and affect appearance, including age and environment. Knowing the skin type helps to choose the products best suited to the needs of the skin and therefore the right skin care. Recently, the increasing demand for cosmetics and the scarcity of well-equipped cosmetologists have encouraged cosmetology centers to meet the need by using artificial intelligence applications. Deep learning applications can give high accuracy results in determining the skin type. Recent research shows that learning performs better on nonlinear data than machine learning methods. The aim of this study is to find the best classification model for skin type prediction in skin analysis data with deep learning. For this purpose, 4 different optimization algorithms as Sgd, Adagrad, Adam and Adamax; Tanh and ReLU activation functions and combinations of different neuron numbers using, 16 different models were created.In experimental studies, the performance of the models varies according to the parameters, and it has been observed that the most successful deep neural network model is the model consisting of 64 neurons, Sgd optimization function and ReLU activation function combination with a success rate of 93.75. The accuracy result obtained has a higher classification success compared to other methods, and shows that deep neural networks can make an accurate skin type classification.

References

  • Seda S.(2019) Kozmetolojik Cilt Analizi http://www.sedasakaci.com/tr/ScientificAnalysis Access Date: 12.04.2021
  • Ashqar, B. A., Abu-Nasser, B. S., & Abu-Naser, S. S. (2019). Plant Seedlings Classification Using Deep Learning.
  • Alarifi J.S., Goyal M., Davison A.K., Dancey D., Khan R., Yap M.H. (2017) Facial Skin Classification Using Convolutional Neural Networks. In: Karray F., Campilho A., Cheriet F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science, vol 10317. Springer, Cham
  • Sun Gyoo Park, Young Deuk Kim, Jin Jun Kim, Seh Hoon Kang, Two possible classifications of facial skin type by two parameters in Korean women: sebum excretion rate (SER) and skin surface relief (SSR), First published: 27 October 2006, https://doi.org/10.1111/j.1600-0846.1999.tb00130.x , Skin Research and Technology
  • Hiroko Kumagai, Kazumi Shioya, Kiyoshi Kawasaki, Izumi Horii, Junichi Koyara, Yasuhisa Nakayama, Wataru Mori, Saburo Ohta, Development of a Scientific Method for Classification of Facial Skin Types, Journal of Society of Cosmetic Chemists of Japan, 1985, Volume 19, Issue 1, Pages 9-19
  • Li, Y., Nie, X., & Huang, R., Web spam classification method based on deep belief networks, Expert Systems with Applications, 96, 261-270, 2018.
  • Bengio, Y., Learning deep architectures for AI. Foundations and trends in Machine Learning, 2 (1), 1- 127, 2009.
  • Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., & De Vos, M. (2018). Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering, 66(5), 1285-1296.
  • Li, D., Huang, F., Yan, L., Cao, Z., Chen, J., & Ye, Z. (2019). Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: Comparisons with multilayer-perceptron-only, bp neural network, and information value models. Applied Sciences, 9(18), 3664.
  • Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I., & Mrzljak, V. (2020). Modeling the spread of COVID-19 infection using a multilayer perceptron. Computational and mathematical methods in medicine, 2020.
  • Arı, A., & Berberler, M. E. (2017). Yapay sinir ağları ile tahmin ve sınıflandırma problemlerinin çözümü için arayüz tasarımı. Acta Infologica, 1(2), 55-73.
  • A. V. Savchenko, "Probabilistic Neural Network With Complex Exponential Activation Functions in Image Recognition," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 651-660, Feb. 2020, doi: 10.1109/TNNLS.2019.2908973.
  • E. M. Dogo, O. J. Afolabi, N. I. Nwulu, B. Twala and C. O. Aigbavboa, "A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks," 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Belgaum, India, 2018, pp. 92-99, doi: 10.1109/CTEMS.2018.8769211.
  • Quang, D., Guan, Y., & Parker, S. C. (2018). YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU. Bioinformatics, 34(20), 3578-3580.
  • Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and libraries for deep learning. Journal of digital imaging, 30(4), 400-405.
  • Parvat, A., Chavan, J., Kadam, S., Dev, S., & Pathak, V. (2017, January). A survey of deep-learning frameworks. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1-7). IEEE.
  • Wang, Z., Yan, M., Chen, J., Liu, S., & Zhang, D. (2020, November). Deep learning library testing via effective model generation. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 788-799).
  • Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and libraries for deep learning. Journal of digital imaging, 30(4), 400-405.
  • Chung, Y., Ahn, S., Yang, J., & Lee, J. (2017). Comparison of deep learning frameworks: about theano, tensorflow, and cognitive toolkit. Journal of Intelligence and Information Systems, 23(2), 1-17.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Fatma Betül Kara 0000-0003-0623-1283

Resul Kara 0000-0001-8902-6837

Seda Sakacı Çelik

Publication Date April 30, 2023
Published in Issue Year 2023 Volume: 11 Issue: 2

Cite

APA Kara, F. B., Kara, R., & Sakacı Çelik, S. (2023). Skin Type Detection with Deep Learning: A Comparative Analysis. Duzce University Journal of Science and Technology, 11(2), 729-742. https://doi.org/10.29130/dubited.930096
AMA Kara FB, Kara R, Sakacı Çelik S. Skin Type Detection with Deep Learning: A Comparative Analysis. DUBİTED. April 2023;11(2):729-742. doi:10.29130/dubited.930096
Chicago Kara, Fatma Betül, Resul Kara, and Seda Sakacı Çelik. “Skin Type Detection With Deep Learning: A Comparative Analysis”. Duzce University Journal of Science and Technology 11, no. 2 (April 2023): 729-42. https://doi.org/10.29130/dubited.930096.
EndNote Kara FB, Kara R, Sakacı Çelik S (April 1, 2023) Skin Type Detection with Deep Learning: A Comparative Analysis. Duzce University Journal of Science and Technology 11 2 729–742.
IEEE F. B. Kara, R. Kara, and S. Sakacı Çelik, “Skin Type Detection with Deep Learning: A Comparative Analysis”, DUBİTED, vol. 11, no. 2, pp. 729–742, 2023, doi: 10.29130/dubited.930096.
ISNAD Kara, Fatma Betül et al. “Skin Type Detection With Deep Learning: A Comparative Analysis”. Duzce University Journal of Science and Technology 11/2 (April 2023), 729-742. https://doi.org/10.29130/dubited.930096.
JAMA Kara FB, Kara R, Sakacı Çelik S. Skin Type Detection with Deep Learning: A Comparative Analysis. DUBİTED. 2023;11:729–742.
MLA Kara, Fatma Betül et al. “Skin Type Detection With Deep Learning: A Comparative Analysis”. Duzce University Journal of Science and Technology, vol. 11, no. 2, 2023, pp. 729-42, doi:10.29130/dubited.930096.
Vancouver Kara FB, Kara R, Sakacı Çelik S. Skin Type Detection with Deep Learning: A Comparative Analysis. DUBİTED. 2023;11(2):729-42.