粒子群优化
人工智能
模式识别(心理学)
模糊逻辑
模糊聚类
聚类分析
计算机科学
数据挖掘
数学
机器学习
作者
Swathi Jamjala Narayanan,Rajkumar Soundrapandiyan,Boominathan Perumal,Cyril Joe Baby
出处
期刊:Smart innovation, systems and technologies
日期:2018-10-02
卷期号:: 305-313
被引量:7
标识
DOI:10.1007/978-981-13-1921-1_31
摘要
Automated classification of medical images using machine learning methods has portrayed a vital role in the field of medical diagnosis. In the research work presented in this paper, the process of classifying the medical image is done in twofold, feature extraction and classification using fuzzy decision tree (FDT) with evolutionary clustering. The feature descriptors of the images are extracted using local diagonal extrema pattern (LDEP). The extracted features are passed to fuzzy particle swarm optimization (FPSO) clustering algorithm to obtain optimal fuzzy partition space for each attribute, which are then later used for inducing FDT. The proposed method of applying FPSO to develop input fuzzy space for Fuzzy ID3 is tested on emphysema CT image to classify the patient’s lung tissue into normal, centribulor emphysema, and paraseptal emphysema. From the results obtained, we observe that our proposed framework improves the classification accuracy of Fuzzy ID3 compared to the other frameworks considered.
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