基于k -最近邻GLCM特征提取的肺炎分类

Suharyana Suharyana, Fuad Anwar, Armylia Chandra Dewi, Mohtar Yunianto, Umi Salamah, Rifai Chai
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引用次数: 0

摘要

<p class="摘要">利用机器学习检测肺炎。本研究的阶段从预处理开始,分为4个阶段:调整大小,裁剪,使用高通滤波器滤波,以及自适应直方图均衡化。继续对22个灰度共生矩阵(GLCM)特征进行特征提取,并使用k -最近邻(KNN)进行分类。使用的图像是150个数据集,以50:50:50的比例进行3类分类的训练,而两个类的训练是50个细菌性肺炎和50个病毒性肺炎。使用GLCM上的角度方向得到了最优的训练数据准确率结果,即1350与KNN分类(k = 3)。对于使用40个数据集的两类分类,准确率为91%,而对于使用60个数据集的三类,准确率为83.3%。</p>
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pneumonia Classification Based on GLCM Features Extraction using K-Nearest Neighbor

Pneumonia has been detected using Machine learning. The stages in this study began with preprocessing in 4 stages: resizing, cropping, filtering using a high pass filter, and Adaptive Histogram Equalization. The feature extraction process continued with 22 Gray Level Co-occurrence Matrix (GLCM) features and classification using K-Nearest Neighbor (KNN). The image used was 150 data sets for training on the classification of 3 classes with a ratio of 50:50:50 while training on two classes was 50 bacterial pneumonia and 50 viral pneumonia. The most optimal training data accuracy results were obtained using the angle direction on the GLCM, namely 135o with the KNN classification (k = 3). For the classification of two classes Using 40 data sets, an accuracy of 91% was obtained, while testing for three classes with 60 data sets was 83.3%.

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