Acne Detection and Classification System
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2019 by IJCTT Journal|
|Year of Publication : 2019|
|Authors : T M Geethanjali, Apoorva M, Noopur G, Priyadarshini H S, Varun P|
|DOI : 10.14445/22312803/IJCTT-V67I5P109|
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.
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.
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Image Processing, Acne Detection, Acne Classification, Adaptive Threshold, Adaptively Regularized Fuzzy C- Means (ARFCM) clustering:, Haralick features, Naive bayes.