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首页> 外文期刊>International journal of synthetic emotions >Organ-Based Medical Image Classification Using Support Vector Machine
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Organ-Based Medical Image Classification Using Support Vector Machine

机译：支持向量机的基于器官的医学图像分类

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Computer-Aided Detection/Diagnosis (CAD) through artificial Intelligence is emerging ara in Medical Image processing and health care to make the expert systems more and more intelligent. The aim of this paper is to analyze the performance of different feature extraction techniques for medical image classification problem. Efforts are made to classify Brain MRI and Knee MRI medical images. Gray Level Co-occurrence Matrix (GLCM) based texture features, DWT and DCT transform features and Invariant Moments are used to classify the data. Experimental results shown that the proposed system produced better results however the training data is less than testing data. Support Vector Machine classifier with linear kernel produced higher accuracy 100% when used with texture features.
机译：通过人工智能进行的计算机辅助检测/诊断（CAD）正在医学图像处理和医疗保健领域崭露头角，以使专家系统变得越来越智能。本文的目的是分析医学图像分类问题中不同特征提取技术的性能。努力对脑部MRI和膝部MRI医学图像进行分类。基于灰度共生矩阵（GLCM）的纹理特征，DWT和DCT变换特征以及不变矩用于对数据进行分类。实验结果表明，所提出的系统产生了更好的结果，但是训练数据少于测试数据。与纹理特征一起使用时，带有线性核的Support Vector Machine分类器可产生100％的更高精度。

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