Organ-Based Medical Image Classification Using Support Vector Machine

Monali Y. Khachane
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引用次数: 21

Abstract

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.
基于器官的支持向量机医学图像分类
基于人工智能的计算机辅助检测/诊断CAD是医学图像处理和医疗保健领域的新兴趋势,使专家系统越来越智能化。本文的目的是分析不同的特征提取技术对医学图像分类问题的性能。对脑MRI和膝关节MRI医学图像进行了分类。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对数据进行分类。实验结果表明,该系统在训练数据少于测试数据的情况下,取得了较好的效果。采用线性核的支持向量机分类器与纹理特征结合使用,准确率达到100%。
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