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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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一个多模型深度学习架构用于诊断多类皮肤疾病.

Mohamed Badr1, Abdullah Elkasaby1, Mohammed Alrahmawy1

  • 1Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.

Journal of imaging informatics in medicine
|November 1, 2024
PubMed
概括

这项研究引入了一种新的深度学习多模型架构,用于精确的皮肤疾病诊断. 这种先进的系统在识别皮肤癌和亚托邦性皮肤炎等疾病方面取得了很高的准确性,改善了患者的护理.

关键词:
深度学习是一种深度学习.皮肤疾病 皮肤疾病转移学习转移学习一个Xception模型.

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科学领域:

  • 皮肤病学 皮肤病学
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 皮肤疾病是全球主要的健康问题,影响着人口的很大一部分.
  • 准确和及时的诊断对于有效的治疗和更好的患者结果至关重要.
  • 现有的诊断方法可能是有限的,需要先进的工具.

研究的目的:

  • 开发和评估一种新的深度学习多模型架构,用于高精度皮肤疾病诊断.
  • 将皮肤病变分为特定的类别,包括亚托邦性皮肤炎,和,皮肤癌和状况.
  • 通过专门的分类模型的转移学习来提高诊断准确性.

主要方法:

  • 一个五类Xception模型被用于皮肤病变的初始分类.
  • 该模型在一个包含25 010张图像的数据集上进行了训练.
  • 转移学习被用来创建专门的模型,以提高40种不同的皮肤状况的准确性.

主要成果:

  • 最初的多模型实现了95%的准确性和99.4%的AUROC.
  • 专门的模型表现出高性能:皮肤癌 (94.0%准确率,99.5%AUROC),亚托皮炎 (91.8%准确率,98.8%AUROC),和粉红发疹 (90.0%准确率,99.0%AUROC) 和牛肉 (90.0%准确率,98.9%AUROC).
  • 开发的方法在诊断全面性方面超过了以前的研究.

结论:

  • 深度学习的多模型架构在皮肤疾病诊断方面取得了重大进展.
  • 该系统为识别各种皮肤病症提供了高准确度和可靠性.
  • 该研究的代码在GitHub上公开提供,以确保可复制性和进一步研究.