Acne detection, assessment, grading and classification using machine learning techniques: a review

Pooja Dhakad, S. Tiwari
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引用次数: 0

Abstract

Acne is one of the most common problems faced by huge population (above 90%) at different age groups, genders and different area of acne and its severity. Among all acne types, acne vulgaris is most common of them. Acne vulgaris has become an interesting domain for researchers in biomedical engineering as well as image processing. Recognizing acne region and skin areas accurately is really challenging task.This plays a major role in acne detection, grading, classification, acne severity detection and automatic acne assessment. This paper presents a comprehensive review which aim to fill the research gap in literature by providing all the state-of-the-art methods applied till date on acne vulgaris images. This research area is least explored and hence this paper focuses on survey of various image processing and machine learning techniques applied on acne images. Future scope and the problems identified in this domain are also elaborated.
使用机器学习技术检测、评估、分级和分类痤疮:综述
痤疮是广大人群(90%以上)在不同年龄组、性别和不同痤疮部位及其严重程度所面临的最常见问题之一。在所有类型的痤疮中,寻常性痤疮是最常见的。寻常痤疮已成为生物医学工程和图像处理研究人员感兴趣的领域。准确识别痤疮区域和皮肤区域确实是一项具有挑战性的任务。这在痤疮检测、分级、分类、痤疮严重程度检测和痤疮自动评估中起着重要作用。本文提出了一个全面的审查,其目的是填补研究空白,在文献提供所有的国家的最先进的方法应用到寻常痤疮图像的日期。这一研究领域是探索最少的,因此本文着重于调查各种图像处理和机器学习技术在痤疮图像上的应用。未来的范围和问题确定在这一领域也进行了阐述。
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