A Random Forest classifier-based approach in the detection of abnormalities in the retina
A Random Forest classifier-based approach in the detection of abnormalities in the retina
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DOI：
10.1007/s11517-018-1878-0
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发表时间：
2019-01-01
影响因子：
3.2
通讯作者：
Banerjee, Sreeparna
中科院分区：
文献类型：
--
作者：
Chowdhury, Amrita Roy;Chatterjee, Tamojit;Banerjee, Sreeparna
使用基于计算机的方法对医学图像中的异常进行分类是医学成像领域越来越感兴趣的问题。需要及时发现糖尿病视网膜病变和老年性黄斑变性引起的异常，以防止疾病的预后。眼科医生和研究人员对使用机器学习的计算机辅助系统越来越感兴趣。我们在这里介绍了一种这样的技术，随机森林分类器，以帮助医生准确诊断疾病。提出了一种用于检测视网膜异常的计算机辅助诊断系统，该系统结合了基于K均值的视网膜图像分割，经过适当的预处理，然后使用机器学习技术，利用几个低层和统计特征。被归类的视网膜异常是由年龄相关性黄斑变性和糖尿病视网膜病变引起的。分析中使用的绩效衡量标准是准确性、敏感性、特异性、F度量和马太相关系数。与另一种机器学习技术的比较表明，随机森林分类器的分类正确率为93.58%，而朴素贝叶斯分类器的正确率为83.63%。
Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases. A computer-aided diagnosis system is proposed for detecting retina abnormalities, which combines K means-based segmentation of the retina image, after due preprocessing, followed by machine learning techniques, using several low level and statistical features. Abnormalities in the retina that are classified are caused by age-related macular degeneration and diabetic retinopathy. Performance measures used in the analysis are accuracy, sensitivity, specificity, F-measure, and Mathew correlation coefficient. A comparison with another machine learning technique, the Naive Bayes classifier shows that the classification achieved by Random Forest classifier is 93.58% and it outperforms Naive Bayes classifier which yields an accuracy of 83.63%.