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黑色素瘤的计算机辅助诊断:现有知识与策略综述

Computer-aided Diagnosis of Melanoma: A Review of Existing Knowledge and Strategies.

作者信息

Maiti Ananjan, Chatterjee Biswajoy, Ashour Amira S, Dey Nilanjan

机构信息

Department of Information Technology, Techno India College of Technology, Kolkata, India.

Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India.

出版信息

Curr Med Imaging. 2020;16(7):835-854. doi: 10.2174/1573405615666191210104141.

Abstract

Computer-aided diagnosis (CAD) systems are the best alternative for immediate disclosure and diagnosis of skin diseases. Such systems comprise several image processing procedures, including segmentation, feature extraction and artificial intelligence (AI) based methods. This survey highlights different CAD methodologies for diagnosing Melanoma and related skin diseases. It has also discussed types, stages, treatments and various imaging techniques of skin cancer. Currently, researchers developed new techniques to detect each stage. Extensive studies on melanoma cancer detection were performed by incorporating advanced machine learning. Still, there is a high need for an accurate, faster, affordable, portable methodology for a CAD system. This will strengthen the work in related fields and address the future direction of a similar kind of research.

摘要

计算机辅助诊断(CAD)系统是皮肤病即时发现与诊断的最佳选择。此类系统包含多个图像处理程序,包括分割、特征提取以及基于人工智能(AI)的方法。本综述着重介绍了用于诊断黑色素瘤及相关皮肤病的不同CAD方法。还讨论了皮肤癌的类型、阶段、治疗方法及各种成像技术。目前,研究人员开发了用于检测各个阶段的新技术。通过纳入先进的机器学习对黑色素瘤检测进行了广泛研究。然而,对于CAD系统而言,仍迫切需要一种准确、快速、经济实惠且便于携带的方法。这将加强相关领域的工作,并指明类似研究的未来方向。

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