Ashour Amira S, Hawas Ahmed Refaat, Guo Yanhui
1Department of Electronics and Electrical Communication Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.
2Department of Computer Science, University of Illinois at Springfield, Springfield, IL USA.
Health Inf Sci Syst. 2018 Aug 20;6(1):7. doi: 10.1007/s13755-018-0047-z. eCollection 2018 Dec.
Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of schistosomiasis infection due to the host's granulomatous cell-mediated immune. Irreversible fibrosis results from the progress of the schistosomal hepatopathy. Sensitive diagnosis of this disease is based on the investigation of the microscopy images, liver tissues, and egg identification. Early diagnosis of schistosomiasis at its initial infection stage is vital to avoid egg-induced irreparable pathological reactions. Typically, there are several classification approaches that can be used for liver fibrosis staging. However, it is unclear which approaches can achieve high accuracy for analyzing and intelligently classifying the liver microscopic images. Consequently, this work aims to study the performance of the different machine learning classifiers for accurate fibrosis level staging of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The classifiers include a multi-layer perceptron neural network, a decision tree, discriminant analysis, support vector machine (SVM), nearest neighbor, and the ensemble of classifiers. The statistical features of the microscopic images are extracted from the different fibrosis levels of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The results established that the maximum achieved classification accuracies of value 90% were achieved using the subspace discriminant ensemble, the quadratic SVM, the linear SVM, or the linear discriminant classifiers. However, the linear discriminant classifier can be considered the superior classifier as it realized the best area under the curve of value 0.96 during the classification of the cellular granuloma as well as the fibro-cellular granuloma fibrosis levels.
肝血吸虫病是一种慢性疾病，主要由血吸虫感染的可溶性虫卵抗原引起，这是由于宿主的肉芽肿细胞介导免疫反应所致。血吸虫性肝病的进展会导致不可逆的纤维化。这种疾病的敏感诊断基于显微镜图像、肝组织检查和虫卵鉴定。在初始感染阶段对血吸虫病进行早期诊断对于避免虫卵引起的不可修复的病理反应至关重要。通常，有几种分类方法可用于肝纤维化分期。然而，尚不清楚哪种方法能够在分析和智能分类肝脏显微镜图像时实现高精度。因此，这项工作旨在研究不同机器学习分类器在准确划分肉芽肿纤维化水平方面的性能，即细胞性、纤维细胞性和纤维化肉芽肿以及正常样本。这些分类器包括多层感知器神经网络、决策树、判别分析、支持向量机（SVM）、最近邻算法以及分类器集成。显微镜图像的统计特征是从肉芽肿的不同纤维化水平中提取的，即细胞性、纤维细胞性和纤维化肉芽肿以及正常样本。结果表明，使用子空间判别集成、二次SVM、线性SVM或线性判别分类器可实现最高90%的分类准确率。然而，线性判别分类器可被视为 superior classifier，因为在对细胞性肉芽肿以及纤维细胞性肉芽肿纤维化水平进行分类时，它实现了最佳的曲线下面积值0.96。 （注：原文中superior classifier未翻译，可能是特定术语，需根据上下文确定准确含义）
