牙源性的
囊肿
支持向量机
牙源性囊肿
人工智能
模式识别(心理学)
计算机科学
逻辑回归
集合(抽象数据类型)
数据集
生物
病理
医学
机器学习
程序设计语言
作者
A. Frydenlund,Mark Eramian,Tom D. Daley
标识
DOI:10.1016/j.compmedimag.2013.12.002
摘要
Odontogenic cysts originate from remnants of the tooth forming epithelium in the jaws and gingiva. There are various kinds of such cysts with different biological behaviours that carry different patient risks and require different treatment plans. Types of odontogenic cysts can be distinguished by the properties of their epithelial layers in H&E stained samples. Herein we detail a set of image features for automatically distinguishing between four types of odontogenic cyst in digital micrographs and evaluate their effectiveness using two statistical classifiers - a support vector machine (SVM) and bagging with logistic regression as the base learner (BLR). Cyst type was correctly predicted from among four classes of odontogenic cysts between 83.8% and 92.3% of the time with an SVM and between 90 ± 0.92% and 95.4 ± 1.94% with a BLR. One particular cyst type was associated with the majority of misclassifications. Omission of this cyst type from the data set improved the classification rate for the remaining three cyst types to 96.2% for both SVM and BLR.
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