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Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection

机译：使用机器学习和邻域特征选择的分层鳞状上皮活检图像分类器

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Squamous cell carcinoma (SCC) of oral cavity is the most common among oral cancer patients. In this paper, we have developed machine learning based automatic oral squamous cell carcinoma (OSCC) classifier named as Stratified Squamous Epithelial Biopsy Image Classifier (SSE-BIC) to categorize H&E-stained microscopic images of squamous epithelial layer in four different classes: normal, well-differentiated, moderately-differentiated and poorly-differentiated. Five classifiers are used to perform the classification by maximum voting method. Total 305 features are extracted from the images of oral mucosa which include color features, textural features, gradient features, geometrical features and tamura features. Unsupervised data mining is used for segmenting the cellular area to compute geometrical features of the cells retaining color details of the images. Feature selection has been performed by neighborhood component feature selection (NCFS) technique. Total 676 images have been used to design, train and test the classifier. A detailed performance analysis is presented with individual feature sets and hybrid feature sets with feature selection applied using individual classifiers as well as proposed classifier. The proposed classifier achieves overall accuracy of 95.56%. This can account for first level of automatic screening of the biopsy images. (C) 2019 Elsevier Ltd. All rights reserved.
机译：口腔鳞状细胞癌（SCC）是口腔癌患者中最常见的。在本文中，我们开发了基于机器学习的自动口腔鳞状细胞癌（OSCC）分类器，称为分层鳞状上皮活检图像分类器（SSE-BIC），以将H＆E染色的鳞状上皮层显微图像分为四个不同类别：正常，高分化，中分化和低分化。五个分类器用于通过最大投票方法进行分类。从口腔粘膜的图像中提取了总共305个特征，包括颜色特征，纹理特征，渐变特征，几何特征和tamura特征。无监督数据挖掘用于分割细胞区域，以计算保留图像颜色细节的细胞的几何特征。已经通过邻域组件特征选择（NCFS）技术执行了特征选择。总共676张图像已用于设计，训练和测试分类器。给出了详细的性能分析，其中包括单个特征集和混合特征集，以及使用单个分类器和建议分类器进行特征选择的情况。拟议的分类器实现了95.56％的整体准确性。这可以解释自动检查活检图像的第一级。 （C）2019 Elsevier Ltd.保留所有权利。

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