Hypergraph Based Feature Selection Technique for Medical Diagnosis

The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K – Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K – Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.     

Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis

The aim of this research is to combine the feature selection (FS) and optimization algorithms as the optimal tool to improve the learning performance like predictive accuracy of the Wisconsin Breast Cancer Dataset classification. An ensemble of the reduced data patterns based on FS was used to train a neural network (NN) using the Levenberg–Marquardt (LM) and the Particle Swarm Optimization (PSO) algorithms to devise the appropriate NN training weighting parameters, and then construct an effective Neural Network classifier to improve the Wisconsin Breast Cancers’ classification accuracy and efficiency. Experimental results show that the accuracy and AROC improved emphatically, and the best performance in accuracy and AROC are 98.83% and 0.9971, respectively.     

基于主动被动近邻算法的肺癌计算机辅助诊断

CT图像的肺癌计算机辅助诊断一般可分为三大模块,即：CT的图像处理、肺肿块的特征提取,以及使用智能分类器对肿块的分类和诊断.研究主要实现计算机CT图像辅助诊断的最后一个模块的计算机化,为医生在最后诊断环节上提供一些参考信息.项目首先需要收集病例,当拿到一个病例,并通过医生辨认出结节后,医生再提供辨认出结节的特征,包括大小、数目、毛刺、分叶等16个.研究共收集到204个恶性病例和46个良性病例,共250个病例,并确定了它们的特征.这些特征通过翻译,转换为一组数字信号,即以数字向量来表示病例.把250个向量输入到新开发的主动被动近邻算法中进行分类诊断,结果显示,对204个恶性病例和46个良性病例进行分类的准确度在90%以上.     

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

Accurate classifiers are vital to design precise computer aided diagnosis (CADx) systems. Classification performances of machine learning algorithms are sensitive to the characteristics of data. In this aspect, determining the relevant and discriminative features is a key step to improve performance of CADx. There are various feature extraction methods in the literature. However, there is no universal variable selection algorithm that performs well in every data analysis scheme. Random Forests (RF), an ensemble of trees, is used in classification studies successfully. The success of RF algorithm makes it eligible to be used as kernel of a wrapper feature subset evaluator. We used best first search RF wrapper algorithm to select optimal features of four medical datasets: colon cancer, leukemia cancer, breast cancer and lung cancer. We compared accuracies of 15 widely used classifiers trained with all features versus to extracted features of each dataset. The experimental results demonstrated the efficiency of proposed feature extraction strategy with the increase in most of the classification accuracies of the algorithms.     

A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases

In biomedical studies, accuracy of classification algorithms used in disease diagnosis systems is certainly an important task and the accuracy of system is strictly related to extraction of discriminatory features from data. In this paper, we propose a new multi-class feature selection method based on Rotation Forest meta-learner algorithm. The feature selection performance of this newly proposed ensemble approach is tested on Erythemato-Squamous diseases dataset. The discrimination ability of selected features is evaluated by the use of several machine learning algorithms. In order to evaluate the performance of Rotation Forest Ensemble Feature Selection approach quantitatively, we also used various and widely utilized ensemble algorithms to compare effectiveness of resultant features. The new multi-class or ensemble feature selection algorithm exhibited promising results in eliminating redundant attributes. The Rotation Forest selection based features demonstrated accuracies between 98% and 99% in various classifiers and this is a quite high performance for Erythemato-Squamous Diseases diagnosis.     

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs).Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.     

利用基因表达谱提取乳腺癌细胞分化相关特征基因

目的 利用基因芯片数据挖掘识别与乳腺癌组织学分级相关的特征基因,对乳腺癌的临床诊断和生物医学研究起到借鉴和参考作用.方法 从公共基因芯片数据库GEO(gene expression omnibus)获得乳腺癌芯片表达数据,利用支持向量机提取获得不同组织学分级的肿瘤样本的特征基因,并对这些基因进行生物学功能分析.结果 获得了64个特征基因,分类正确率达到100%,这些基因与癌症有较大的相关性,主要集中在转录调控、离子运输、器官发生发育等多个生物学途径中.结论 通过对基因芯片数据的挖掘,可以从全局上了解肿瘤的表达情况,加深对乳腺癌细胞分化分子机制的认识.     

肿瘤标记物Cytokeratin19诊断肺癌的临床价值

目的：探讨细胞角蛋白 １９（ Ｃ Ｋ１９）作为肺癌指标的临床价值。方法：应用 Ｃｅｎｔｏｃｏｒ Ｃ Ｙ Ｆ Ｒ Ａ２１１ 放射免疫分析药盒测定肺癌病人血清细胞角蛋白１９（ Ｃ Ｋ１９）的数值。结果：健康人 Ｃ Ｋ１９ 的平均值为 ０．６７ ｎｇ／ｍ ｌ，肺腺癌（Ⅲ、Ⅳ期） Ｃ Ｋ１９ 的阳性率为４１．７％ ，肺鳞状上皮癌（Ⅲ、Ⅳ期）为６９．２％ ，大细胞肺癌为２５．０％ ，小细胞肺癌为 ３３．３％ 。 Ｃ Ｋ１９ 比 Ｓ Ｃ Ｃ抗原具有较高的阳性率。结论： Ｃ Ｋ１９ 对肺鳞状上皮癌具有较高的诊断价值。     

基于宗族进行裂变选择的自适应遗传算法

提出了一种改进的自适应遗传算法,在选择算子中引入裂变选择的思想,避免种群中超级个体的出现,维持了种群的多样性。该算法改造了交叉算子和变异算子,提高了算法的收敛速度,避免早熟。同时,提出了在宗族中构造子代种群的思想,提高了算法的寻优效率。仿真函数优化的结果验证了该算法能有效地维持种群的多样性并迅速找到最优解。     

Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

Tuberculosis is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly Mycobacterium tuberculosis (Wikipedia 2009). It is a great problem for most developing countries because of the low diagnosis and treatment opportunities. Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism. Thus, tuberculosis is a great health concern all over the world, and in Turkey as well. This article presents a study on tuberculosis diagnosis, carried out with the help of multilayer neural networks (MLNNs). For this purpose, an MLNN with two hidden layers and a genetic algorithm for training algorithm has been used. The tuberculosis dataset was taken from a state hospital’s database, based on patient’s epicrisis reports.     

基于开放式遗传算法的图像阈值选取

引入了开放式遗传算法的理论，使种群在一个开放的环境中进化，增加了种群的多样性。同时对交叉、变异操作进行了改进，避免了进化过程中种群的退化现象，从而有效克服了遗传算法的早熟问题又提高了遗传算法的收敛性能。文章以最小误差法为例，对比了本文算法和简单遗传算法在阈值处理中的性能，并用实验证明了本文算法的可行性。     

恶性肿瘤特异性生长因子对肺癌早期诊断的价值

目的本研究旨在评价恶性肿瘤特异性生长因子（tumor specific growth factors,TSGF）对肺癌早期诊断价值。方法采用比色法测定21例肺癌、19例肺良性病变患者外周血中的TSGF水平和放射免疫法测定血癌胚抗原（carcinoembryonic antigen,CEA）水平。结果肺癌组的TSGF水平（63．48±20．39）U／ml显著高于肺良性疾病组（40．42±13．61）U／ml（P〈0．01）,肺癌组CEA的水平（17．76±11．28）ng／ml同样也高于肺良性疾病组（11．26±4．43）ng／ml（P〈0．05）。TSGF对肺癌诊断的敏感性明显高于CEA（P〈0．05）,诊断的特异性与CEA差异无统计学意义（P〉0．05）。结论外周血中的TSGF有较高的敏感性及特异性,有助于肺癌的早期诊断及鉴别诊断。     

复合型肺癌的诊治策略

1病历摘要 患者女性,1956年11月出生。患者2012年初开始出现咳嗽．因病情持续未缓解至当地医院就诊,2012—12-24在江门市中心医院检查,胸部CT提示右肺上叶后段肿块,     

肺癌早期诊断的策略和意义

在我国,随着人均寿命延长、城市化加快、禁烟运动不力、职业暴露、环境污染、遗传易感性等因素的影响,肺癌的发病率呈上升趋势,目前已占全部恶性肿瘤的首位。现今每4例死亡者中有1例为肺癌。根据卫生部全国肿瘤防治研究办公室统计,我国男性肺癌死亡数1991年为134748,2000年为251839,2005年为3322286;女性肺癌死亡数1991年为56468,2000年为119648,2005年为165 622。     

Computerized Lung Nodule Detection Using 3D Feature Extraction and Learning Based Algorithms

In this paper, a Computer Aided Detection (CAD) system based on three-dimensional (3D) feature extraction is introduced to detect lung nodules. First, eight directional search was applied in order to extract regions of interests (ROIs). Then, 3D feature extraction was performed which includes 3D connected component labeling, straightness calculation, thickness calculation, determining the middle slice, vertical and horizontal widths calculation, regularity calculation, and calculation of vertical and horizontal black pixel ratios. To make a decision for each ROI, feed forward neural networks (NN), support vector machines (SVM), naïve Bayes (NB) and logistic regression (LR) methods were used. These methods were trained and tested via k-fold cross validation, and results were compared. To test the performance of the proposed system, 11 cases, which were taken from Lung Image Database Consortium (LIDC) dataset, were used. ROC curves were given for all methods and 100% detection sensitivity was reached except naïve Bayes.     

多肿瘤标志物蛋白芯片技术在肺癌诊断中的应用

目的:探讨多肿瘤标志物蛋白芯片技术在肺癌诊断中的应用价值。方法:采用多肿瘤标志物蛋白芯片诊断系统检测30例肺癌、50例良性肺部疾病患者和40例健康体检者的12种常见肿瘤标志物。结果:肺癌组糖抗原(CA)中CA125、CA153、CA242,癌胚抗原(CEA)和铁蛋白(FER)值较其他两组明显升高(P<0.01);联合检测灵敏度80%,灵敏度较单指标检测提高约30%。结论:应用多肿瘤标志物蛋白芯片技术确定CA125、CA242、CA153和CEA的联合检测是筛检肺癌的优化组合,对肺癌的早期诊断有较高的临床应用价值。     

多肿瘤标志物蛋白芯片技术在肺癌诊断中的应用

目的 探讨多肿瘤标志物蛋白芯片技术在肺癌诊断中的应用价值。方法采用多肿瘤标志物蛋白芯片诊断系统检测36例肺癌、60例良性肺部疾病患者和39例健康体检者的12种常见肿瘤标志物。结果肺癌组糖抗原（CA）中CA125、CA153、CA242,癌胚抗原（CEA）和铁蛋白（FER）值较其他两组明显升高（P〈0．01）;联合检测灵敏度80．6％,灵敏度较单指标检测提高约30％。结论应用多肿瘤标志物蛋白芯片技术确定CA125、CA242、CAl53和CEA的联合检测是筛检肺癌的优化组合,对肺癌的早期诊断有较高的临床应用价值。     

多肿瘤标志物蛋白芯片技术在肺癌诊断中的应用

目的探讨多肿瘤标志物蛋白芯片技术在肺癌诊断中的应用价值.方法采用多肿瘤标志物蛋白芯片诊断系统检测36例肺癌、60例良性肺部疾病患者和39例健康体检者的12种常见肿瘤标志物.结果肺癌组糖抗原(CA)中CA125、CA153、 CA242,癌胚抗原(CEA)和铁蛋白(FER)值较其他两组明显升高(P<0.01);联合检测灵敏度80.6%,灵敏度较单指标检测提高约30%.结论应用多肿瘤标志物蛋白芯片技术确定CA 125、CA242、CA153和CEA的联合检测是筛检肺癌的优化组合,对肺癌的早期诊断有较高的临床应用价值.