Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Author:

Madooei Ali1ORCID,Drew Mark S.1

Affiliation:

1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

Abstract

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction.

Publisher

Hindawi Limited

Subject

Radiology Nuclear Medicine and imaging

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DDI-CoCo: A Dataset for Understanding the Effect of Color Contrast in Machine-Assisted Skin Disease Detection;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Strategy for early diagnosis of skin melanoma. Effectiveness of oncodermatology screenings;P.A. Herzen Journal of Oncology;2024

3. Classification of melanonychia, Beau’s lines, and nail clubbing based on nail images and transfer learning techniques;PeerJ Computer Science;2023-08-24

4. Skin Disease Detection using Deep Learning;2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART);2022-12-16

5. Detection of Skin Diseases via Deep Learning using SVM Method;2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART);2022-12-16

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3