Open AccessA performance study of the suitability of Adaptive boosting in Red Acne detection
Author(s) -
Satyake Bakshi,
A. Sathya
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v14.i3.pp1493-1498
Subject(s) - boosting (machine learning) , adaboost , acne , artificial intelligence , computer science , pattern recognition (psychology) , computer vision , dermatology , classifier (uml) , medicine
AdaBoost along with HaarCascades have been well received for its accuracy and performance in primarily Facial Recognition applications. However, they are known to perform poorly with objects which have a different rotational orientation or for objects whose shapes are largely variant . In this paper, we apply Adaptive Cascading technique to a specific dermatological application of detecting red acne which are largely shaped variant outgrowths on the skin and to identify its suitability in the detection of acne. Based on the outcome it would be declared if Viola-Jones based Adaptive Boosting is well suited for dermatological processing of skin diseases.