• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Skin cancer diagnosis using hybrid artificial intelligence system.
【24h】

Skin cancer diagnosis using hybrid artificial intelligence system.

机译：使用混合人工智能系统诊断皮肤癌。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Malignant melanoma is the most lethal form of skin cancer and is responsible for 75% of all deaths from skin cancer. However, malignant melanoma can be treated successfully if detected in the earliest stage.; This project deals with the design and implementation of a hybrid artificial intelligence system, which can help to diagnose malignant melanoma. In this study, a three-stage model is used in the classification system that combines image-processing techniques, expert systems, neural network systems, and fuzzy inference systems to diagnose skin cancer. In Stage I, image-processing techniques are used to extract features that characterize skin tumors; a feature selection process based on the correlation analysis and aided with decision tree is also designed and implemented. In Stage II, the neural networks work as classifiers that are used to classify input images into different tumor categories. Two different hybrid neural network architectures are developed—a voting scheme and a hierarchical structure. In Stage III, the outputs from neural networks are combined by a fuzzy inference system to obtain an improved performance. In this system, an optional expert system which consists of a set of rules based on the statistics of skin tumors and the clinical experience of a dermatologist is used. Furthermore, these two hybrid architectures are combined to achieve the best performance.; This computer-aided diagnostic system is trained and tested with a large number of lesions and promising results are demonstrated. This system also provides a general framework for a hybrid classification system.
机译：恶性黑色素瘤是最致命的皮肤癌形式，占皮肤癌死亡总数的75％。但是，如果尽早发现恶性黑色素瘤，就可以成功治疗。该项目涉及混合人工智能系统的设计和实现，该系统可以帮助诊断恶性黑色素瘤。在这项研究中，分类系统使用了三阶段模型，该模型结合了图像处理技术，专家系统，神经网络系统和模糊推理系统来诊断皮肤癌。在第一阶段，图像处理技术用于提取表征皮肤肿瘤的特征。设计并实现了基于相关分析并辅以决策树的特征选择过程。在阶段II中，神经网络用作分类器，用于将输入图像分类为不同的肿瘤类别。开发了两种不同的混合神经网络架构-投票方案和分层结构。在阶段III中，神经网络的输出通过模糊推理系统进行组合以获得改进的性能。在该系统中，使用了一个可选的专家系统，该系统由一组基于皮肤肿瘤统计数据和皮肤科医生的临床经验的规则组成。此外，将这两种混合体系结构结合在一起以获得最佳性能。该计算机辅助诊断系统经过培训和测试，可检测到大量病灶，并显示出令人鼓舞的结果。该系统还为混合分类系统提供了通用框架。

著录项

  • 作者

    Chang, Ying.;

  • 作者单位

    University of Missouri - Rolla.;

  • 授予单位 University of Missouri - Rolla.;
  • 学科 Engineering Electronics and Electrical.; Health Sciences Oncology.; Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 131 p.
  • 总页数 131
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;肿瘤学;生物医学工程;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号