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    一种基于Bayesian学习的彩色肺癌图像语义描述模型

    A SEMANTIC DESCRIPTION MODEL OF LUNG CANCER CHROMATIC IMAGES BASED ON BAYESIAN LEARNING

    • 摘要: 通过将 Bayesian理论框架引入肺癌分类识别问题 ,提出一种基于 Bayesian学习理论的彩色肺癌图像语义描述模型 .该模型由原始图像层 (raw im age layer,RIL )、图像特征层 (image feature layer,IFL )、语义知识层(sem antic knowledge layer,SKL )以及语义描述算法 SDA构成 .基于此模型提出一种肺癌分类识别算法 ,并实现了一个肺癌分类识别系统 .实验表明 ,该模型具有较高的肺癌分类准确率 ,是行之有效的

       

      Abstract: By introducing the Bayesian framework to the lung cancer diagnosing problem,a semantic description model of lung cancer chromatic images is proposed, which is composed of raw image layer (RIL), image feature layer (IFL), semantic knowledge layer (SKL), and a semantic description algorithm SDA. Based on this model, a lung cancer identification system is successfully implemented.The experiment results also show that this model is very effective to identify different kinds of lung cancer at high correct rates.

       

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