Deteksi Otomatis Jerawat Wajah Menggunakan Metode Convolutional Neural Network (CNN)
The development of cosmetology in the world lately is growing rapidly. These developments are balanced by the emergence of cosmetics and skin care from various brands, but not a few negative effects from use, one of which is acne. Acne is one of the problems on the skin, especially the face that arises physiologically because almost everyone has experienced it (Wasitaatmadja, 2010). Acne consists of various types, namely blackheads, whiteheads, papules and cysts (Bhate, K. & Williams, 2013). Not a few people who want to remove and be free from acne. The current technological developments in the field of image processing in recent years with the application of convolutional neural networks have shown significant performance by having a high level of accuracy, for example object detection which recently had image restoration. Therefore, technological developments to facilitate the treatment of acne are urgently needed by medical personnel, especially dermatologists. This research focuses on developing the accuracy of the method using the hough circle transform & Convolutional Neural Network (CNN) method. This study proves the increase in accuracy and accuracy of the object of acne detection using the Convolutional Neural Network (CNN) method. The results of the learning process obtained a CNN model with an accuracy of 99.8% to 100%, so it can be concluded that the CNN method designed in this study can classify images well.
Shen, Xiaolei & Zhang, Jiachi & Yan, Chenjun & Zhou, Hong. (2018). An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network. Scientific Reports. 8. 10.1038/s41598-018-24204-6.
Yanuangga G.H.L, Lukman Zaman,"DETEKSI JERAWAT OTOMATIS PADA CITRA WAJAH STUDI KASUS PADA KULIT PENDUDUK JAWA", Teknik Informatika Universtas Darul Ulum, Teknik Informatika Sekolah Tinggi Teknik Surabaya, Seminar Nasional “Inovasi dalam Desain dan Teknologi” - IDeaTech 2015
Watcharaporn Sitsawangsopon, Maetawee Juladash,"Medical Image Processing in Automatic Acne Detection for Medical Treatment", School of Information, Computer and Communication Technology,Sirindhorn International Institute of Technology, Thammasat University, 2014
Vasefi, Fartash & Kemp, William & MacKinnon, Nicholas & Amini, Mohammad & Valdebran, Manuel & Huang, Kevin & Zhang, Haomiao. (2018). Automated facial acne assessment from smartphone images. 22. 10.1117/12.2292506.
Fazly Salleh Abas, Benjamin Kaffenberger, Joseph Bikowski, and Metin N. Gurcan "Acne image analysis: lesion localization and classification", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850B (24 March 2016); https://doi.org/10.1117/12.2216444
A. Oualid Djekoune, Khadidja Messaoudi, Kahina Amara (2016) “ Incremental circle hough transform: An improved method for circle detection”, https://doi.org/10.1016/j.ijleo.2016.12.064
Li-sheng Wei, Quan Gan, Tao Ji, "Skin Disease Recognition Method Based on Image Color and Texture Features", Computational and Mathematical Methods in Medicine, vol. 2018, Article ID 8145713, 10 pages, 2018. https://doi.org/10.1155/2018/8145713
T. Shanthi, R.S. Sabeenian, R. Anand, “Automatic diagnosis of skin diseases using convolution neural network”, Microprocessors and Microsystems, Volume 76, 2020, 103074, ISSN 0141-9331, https://doi.org/10.1016/j.micpro.2020.103074.
Patnaik S. K, Sidhu M. S, Gehlot Y, Sharma B, Muthu P. Automated Skin Disease Identification using Deep Learning Algorithm. Biomed Pharmacol J 2018;11(3).
T. A. Rimi, N. Sultana and M. F. Ahmed Foysal, "Derm-NN: Skin Diseases Detection Using Convolutional Neural Network," 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 1205-1209, doi: 10.1109/ICICCS48265.2020.9120925.
Copyright (c) 2021 Fajar Sudana Putra, Kusrini, Mei P Kurniawan
This work is licensed under a Creative Commons Attribution 4.0 International License.