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Acne Detection Using Speeded up Robust Features and Quantification Using K-Nearest Neighbors Algorithm

Authors:

Natchapol Kittigul,

Bunyarit UyyanonvaraAuthors Info & Claims

ICBBS '17: Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

Pages 168 - 171

https://doi.org/10.1145/3121138.3121168

Published: 22 June 2017 Publication History

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Abstract

About 85% of people between the age of 12 and 24 experience acne, the acne treatment cost exceed $3 billion in U.S.A. Currently dermatologist use manual skin assessment method such as visual and photography then manually mark and count acne on patient face which is time consuming and subjective. This paper proposed acne detection method using Speeded Up Robust Features then classified using 5 designed features: Hue Mean, Standard Deviation (SD) of Red, SD of Green, SD of Blue and Circularity. Quantification using K-Nearest Neighbors algorithm (KNN) was also assessed. The result presented 68% accuracy with 73% sensitivity and 84% precision on average.

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Cited By

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Wei XZhang LZhang JWang JLiu WLi JJiang X(2023)Decoupled Sequential Detection Head for accurate acne detectionKnowledge-Based Systems10.1016/j.knosys.2023.111305(111305)Online publication date: Dec-2023
https://doi.org/10.1016/j.knosys.2023.111305
Nazira FShanto SNur KMridha M(2022)Hemifacial Microsomia Detection Using Convolutional Neural NetworksProceedings of the 2nd International Conference on Computing Advancements10.1145/3542954.3543009(384-391)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1145/3542954.3543009
Junayed MIslam MJeny ASadeghzadeh ABiswas TShah A(2022)ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New DatasetIEEE Access10.1109/ACCESS.2021.313802110(1245-1258)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3138021
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Index Terms

Acne Detection Using Speeded up Robust Features and Quantification Using K-Nearest Neighbors Algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections

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ICBBS '17: Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

June 2017

184 pages

ISBN:9781450352222

DOI:10.1145/3121138

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Natl University of Singapore: National University of Singapore

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Published: 22 June 2017

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ICBBS '17: 6th International Conference on Bioinformatics and Biomedical Science

June 22 - 24, 2017

Singapore, Singapore

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Wei XZhang LZhang JWang JLiu WLi JJiang X(2023)Decoupled Sequential Detection Head for accurate acne detectionKnowledge-Based Systems10.1016/j.knosys.2023.111305(111305)Online publication date: Dec-2023
https://doi.org/10.1016/j.knosys.2023.111305
Nazira FShanto SNur KMridha M(2022)Hemifacial Microsomia Detection Using Convolutional Neural NetworksProceedings of the 2nd International Conference on Computing Advancements10.1145/3542954.3543009(384-391)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1145/3542954.3543009
Junayed MIslam MJeny ASadeghzadeh ABiswas TShah A(2022)ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New DatasetIEEE Access10.1109/ACCESS.2021.313802110(1245-1258)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3138021
Borade SKalbande D(2021)Survey paper based critical reviews for Cosmetic Skin Diseases2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)10.1109/ICAIS50930.2021.9395803(580-585)Online publication date: 25-Mar-2021
https://doi.org/10.1109/ICAIS50930.2021.9395803
Javadi NSoltanizadeh H(2021)Automated detection, 3D position of facial skin lesions using genetic algorithm and Kinect cameraComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization10.1080/21681163.2021.197234210:1(48-54)Online publication date: 5-Sep-2021
https://doi.org/10.1080/21681163.2021.1972342
Aryan MKrathu WArpnikanondt CTassaneetrithep BPorkaew KChignell MFong SWatanapa B(2020)Image Recognition for Detecting Hand Foot and Mouth DiseaseProceedings of the 11th International Conference on Advances in Information Technology10.1145/3406601.3406640(1-11)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1145/3406601.3406640

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A Multilevel Thresholding Approach for Acne Detection in Medical Treatment

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A Multilevel Thresholding Approach for Acne Detection in Medical Treatment

Initial Approach to the Identification Degree of Skin Damage and Classification of Acne by YOLOv8

Spatial-based skin detection using discriminative skin-presence features

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