Acne Detection and Classification System

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-5
Year of Publication : 2019
Authors : T M Geethanjali, Apoorva M, Noopur G, Priyadarshini H S, Varun P
  10.14445/22312803/IJCTT-V67I5P109

MLA

MLA Style:T M Geethanjali, Apoorva M, Noopur G, Priyadarshini H S, Varun P"Acne Detection and Classification System" International Journal of Computer Trends and Technology 67.5 (2019): 54-57.

APA Style: T M Geethanjali, Apoorva M, Noopur G, Priyadarshini H S, Varun P (2019). Acne Detection and Classification System International Journal of Computer Trends and Technology, 67(5), 54-57.

Abstract
Acne is a skin disease which forms when hair follicles are blocked with oil or dead skin. In this paper, a functional image processing technique has been developed for acne detection and classification. An image of skin area affected with acne is being considered as the ROI (region of interest). This is taken into the experiment, which results in automatic markings of the acne and thereby extracting their features and performing the classification of the acne. The presence of acne in different parts of the face or body has different indication of skin diseases and might not be dangerous but depends on severity and leaves scar. Detecting different types of acne lesions is very important in both diagnosis as well as management. To access acne, clinicians and dermatologists use methods such as ordinary flash photography and direct visual assessment, which is time consuming. The classification of various acne types considered in this work include- Acne Cystic, Acne Excoriated and Acne Pustular. Experimental output can be summarized with 91.666% accuracy by using 20 images of each type.

Reference
[1] Chantharaphaichi, T., Uyyanonvara, B., Sinthanayothin, C., & Nishihara, A. (2015, March). Automatic acne detection for medical treatment. In Information and Communication Technology for Embedded Systems (IC-ICTES), 2015 6th International Conference of (pp. 1-6). IEEE.
[2] Min, S., Kong, H. J., Yoon, C., Kim, H. C., & Suh, D. H. (2013). Development and evaluation of an automatic acne lesion detection program using digital image processing. Skin Research and Technology, 19(1), e423-e432.
[3] Shen, X., Zhang, J., Yan, C., & Zhou, H. (2018). An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network. Scientific reports, 8(1), 5839.
[4] Amini, M., Vasefi, F., Valdebran, M., Huang, K., Zhang, H., Kemp, W., & MacKinnon, N. (2018, February). Automated facial acne assessment from smartphone images. In Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVI (Vol. 10497, p. 104970N). International Society for Optics and Photonics.
[5] Chin, C. L., Yang, Z. Y., Su, R. C., & Yang, C. S. (2018, September). A Facial Pore Aided Detection System Using CNN Deep Learning Algorithm. In 2018 9th International Conference on Awareness Science and Technology (iCAST) (pp. 90-94). IEEE.
[6] Singh, P., & Saxena, V. (2018, February). Assessing the Scar Images to Check Medical Treatment Effectiveness. In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 624-629). IEEE.
[7] Haddad, A., & Hameed, S. A. (2018, September). Image Analysis Model For Skin Disease Detection: Framework. In 2018 7th International Conference on Computer and Communication Engineering (ICCCE) (pp. 1-4). IEEE.
[8] Kittigul, N., & Uyyanonvara, B. (2017, June). Acne Detection Using Speeded up Robust Features and Quantification Using K-Nearest Neighbors Algorithm. In Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science (pp. 168-171). ACM.
[9] Ajith, A., Goel, V., Vazirani, P., & Roja, M. M. (2017, June). Digital dermatology: Skin disease detection model using image processing. In Intelligent Computing and Control Systems (ICICCS), 2017 International Conference on (pp. 168-173). IEEE.
[10] Alamdari, N., Tavakolian, K., Alhashim, M., & Fazel-Rezai, R. (2016, May). Detection and classification of acne lesions in acne patients: A mobile application. In Electro Information Technology (EIT), 2016 IEEE International Conference on (pp. 0739-0743). IEEE.

Keywords
Image Processing, Acne Detection, Acne Classification, Adaptive Threshold, Adaptively Regularized Fuzzy C- Means (ARFCM) clustering:, Haralick features, Naive bayes.