A Deep Learning Approach Bassed on Classification to Detect Facial Skin Defect
DOI:
https://doi.org/10.57152/predatecs.v2i2.1558Keywords:
Facial Skin, Convolutional Neural Network, Generative Adversarial Networks, Recurrent Neural NetworkAbstract
As people are more active, facial skin is often neglected, which can lead to acne, eye bags, and redness. In this study, deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Networks (GANs) are used to classify facial skin damage. DenseNet201 and MobileNetV2 architectures were also used to evaluate the models in this study. The dataset used consists of facial skin disease photos collected from the Kaggle database. The model was trained and tested to classify the types of skin damage after going through data collection and preprocessing stages. The results showed that the GANs model and the DenseNet201 and MobileNetV2 architectures were the best models, with test accuracy values of 89% for the GANs model, 88% for the DenseNet201 architecture, and 89% for the MobileNetV2 architecture. These results show that deep learning approach techniques can help classify and find facial skin problems well. and it is expected that it will be a great progress in the field of dermatology and skin health.
References
J. L. López-Estebaranz, P. Herranz-Pinto, and B. Dréno, “Consensus-Based Acne Classification System and Treatment Algorithm for Spain,” Actas Dermosifiliogr., vol. 108, no. 2, pp. 120–131, 2017, doi: 10.1016/j.adengl.2016.10.003.
Z. Zhu et al., “Global, regional, and national burdens of acne vulgaris in adolescents and young adults aged 10-24 years from 1990 to 2021: a trend analysis.,” Br. J. Dermatol., Sep. 2024, doi: 10.1093/bjd/ljae352.
I. Muhimmah, N. F. Muchlis, and A. Kurniawardhani, “Automatic Facial Redness Detection on Face Skin Image,” IIUM Eng. J., vol. 22, no. 1, pp. 68–77, 2021, doi: 10.31436/IIUMEJ.V22I1.1495.
S. Saiwaeo, S. Arwatchananukul, L. Mungmai, W. Preedalikit, and N. Aunsri, “Human skin type classification using image processing and deep learning approaches,” Heliyon, vol. 9, no. 11, p. e21176, 2023, doi: 10.1016/j.heliyon.2023.e21176.
G. A. Mesías-Ruiz, I. Borra-Serrano, J. M. Peña, A. I. de Castro, C. Fernández-Quintanilla, and J. Dorado, “Weed species classification with UAV imagery and standard CNN models: Assessing the frontiers of training and inference phases,” Crop Prot., vol. 182, no. May, 2024, doi: 10.1016/j.cropro.2024.106721.
W. Gunawan, D. Suhartono, F. Purnomo, and A. Ongko, “Named-Entity Recognition for Indonesian Language using Bidirectional LSTM-CNNs,” Procedia Comput. Sci., vol. 135, pp. 425–432, 2018, doi: 10.1016/j.procs.2018.08.193.
A. Kumar, N. Gaur, S. Chakravarty, M. H. Alsharif, P. Uthansakul, and M. Uthansakul, “Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs,” Ain Shams Eng. J., vol. 15, no. 3, p. 102505, 2024, doi: 10.1016/j.asej.2023.102505.
D. Hastari, S. Winanda, A. R. Pratama, N. Nurhaliza, and E. S. Ginting, “Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 2, pp. 71–77, 2024, doi: 10.57152/predatecs.v1i2.865.
K. Murali and K. Murali, “ScienceDirect Conference on Enhancing Fake Image Detection?: A Novel Two-Step Approach Combining CNNs Enhancing Fake Image Detection?: A Novel Two-Step Approach Combining GANs and CNNs,” Procedia Comput. Sci., vol. 235, pp. 810–819, 2024, doi: 10.1016/j.procs.2024.04.077.
M. N. Musa and N. O. Badmos, “Application of MobileNetV2 Transfer Learning for Face-shield Detection Application of MobileNetV2 Transfer Learning for Face-shield Detection,” no. July, 2023.
W. Madelina and Sulistiyaningsih, “Review: Resistensi Antibiotik pada Terapi Pengobatan Jerawat,” J. Farmaka, vol. 16, no. 2, pp. 105–117, 2018.
R. T. Lestari et al., “Perilaku Mahasiswa Terkait Cara Mengatasi Jerawat,” J. Farm. Komunitas, vol. 8, no. 1 SE-Articles, pp. 15–19, Oct. 2020, doi: 10.20473/jfk.v8i1.21922.
L. Deng and D. Yu, “Deep Learning: Methods and Applications,” Found. Trends Signal Process., vol. 7, Jan. 2013, doi: 10.1561/2000000039.
M. Dyrmann, H. Karstoft, and H. S. Midtiby, “Plant species classification using deep convolutional neural network,” Biosyst. Eng., vol. 151, pp. 72–80, 2016, doi: https://doi.org/10.1016/j.biosystemseng.2016.08.024.
M. Hussain, J. J. Bird, and D. R. Faria, “A study on CNN transfer learning for image classification,” Adv. Intell. Syst. Comput., vol. 840, no. June, pp. 191–202, 2019, doi: 10.1007/978-3-319-97982-3_16.
J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,” Agric., vol. 11, no. 8, pp. 1–18, 2021, doi: 10.3390/agriculture11080707.
J. Wu, “Introduction to Convolutional Neural Networks,” Introd. to Convolutional Neural Networks, pp. 1–31, 2017, [Online]. Available: https://web.archive.org/web/20180928011532/https://cs.nju.edu.cn/wujx/teaching/15_CNN.pdf.
P. D. Rinanda, D. N. Aini, T. A. Pertiwi, S. Suryani, and A. J. Prakash, “Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 2, pp. 56–61, 2024, doi: 10.57152/predatecs.v1i2.872.
M. A. Hossain and M. S. Alam Sajib, “Classification of Image using Convolutional Neural Network (CNN),” Glob. J. Comput. Sci. Technol., vol. 19, no. 2, pp. 13–18, 2019, doi: 10.34257/gjcstdvol19is2pg13.
J. Wang, Y. Yang, J. Mao, Z. Huang, C. Huang, and W. Xu, “CNN-RNN: A Unified Framework for Multi-label Image Classification,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2285–2294, doi: 10.1109/CVPR.2016.251.
D. Lee et al., “Long short-term memory recurrent neural network-based acoustic model using connectionist temporal classification on a large-scale training corpus,” China Commun., vol. 14, no. 9, pp. 23–31, 2017, doi: 10.1109/CC.2017.8068761.
Y. Kristian, I. K. E. Purnama, E. H. Sutanto, L. Zaman, E. I. Setiawan, and M. H. Purnomo, “Klasifikasi Nyeri pada Video Ekspresi Wajah Bayi Menggunakan DCNN Autoencoder dan LSTM,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 7, no. 3, pp. 308–316, 2018, doi: 10.22146/jnteti.v7i3.440.
J. Arifianto and I. Muhimmah, “Aplikasi Web Pendeteksi Jerawat Pada Wajah Menggunakan Algoritma Deep Learning dengan TensorFlow,” J. Autom., pp. 21–29, 2021.
A. Satriawan, B. Imran, and S. Erniwati, “Identifikasi Kemiripan Foto Asli Dan Sketsa Menggunakan Model Generative Adversarial Networks (GANs),” J. Kecerdasan Buatan dan Teknol. Inf., vol. 2, no. 3, pp. 122–127, 2023.
J. J. Jeong, A. Tariq, T. Adejumo, H. Trivedi, J. W. Gichoya, and I. Banerjee, “Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.,” J. Digit. Imaging, vol. 35, no. 2, pp. 137–152, Apr. 2022, doi: 10.1007/s10278-021-00556-w.
P. Sitompul, H. Okprana, A. Prasetio, and G. Artikel, “Identifikasi Penyakit Tanaman Padi Melalui Citra Daun Menggunakan DenseNet 201 Identification of Rice Plant Diseases Through Leaf Image Using DenseNet 201,” JOMLAI J. Mach. Learn. Artif. Intell., vol. 1, no. 2, pp. 143–150, 2022, doi: 10.55123/jomlai.v1i2.889.
J. Pardede and D. A. L. Putra, “Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 3, 2020, doi: 10.28932/jutisi.v6i3.2814.
A. Dash, P. K. Sethy, and S. K. Behera, “Maize disease identification based on optimized support vector machine using deep feature of DenseNet201,” J. Agric. Food Res., vol. 14, no. October, p. 100824, 2023, doi: 10.1016/j.jafr.2023.100824.
H. K. Chhatrapati et al., “Transfer Learning and Supervised Machine Learning Approach for Detection of Skin Cancer: Performance Analysis and Comparison,” vol. 10, no. September, p. 1, 2021, [Online]. Available: https://www.researchgate.net/publication/354450739.
R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma image classification based on MobileNetV2 network,” Procedia Comput. Sci., vol. 197, pp. 198–207, 2021, doi: 10.1016/j.procs.2021.12.132.
J. S. Talahua, J. Buele, P. Calvopina, and J. Varela-Aldas, “Facial recognition system for people with and without face mask in times of the covid-19 pandemic,” Sustain., vol. 13, no. 12, pp. 1–19, 2021, doi: 10.3390/su13126900.
P. D. Rinanda and Mustakim, "Implementation of PNN, ANN And K-NN Algorithms on Indonesian Marketplace Reviews on Google Play Store," 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Manama, Bahrain, 2024, pp. 1070-1074, doi: 10.1109/ICETSIS61505.2024.10459477.
