Wavelet-based feature extraction for DNA microarray classification

Complementary DNA (cDNA) microarray-based tumor gene expression profiles have been successfully used for cancer diagnosis. The main difficulty in processing cDNA microarrays is the ultra-high dimensionality of the microarrays. In this paper, we approach the dimensionality reduction using a novel wavelet-based approach that extracts classification features through microarray-block processing, thresholding, and averaging of approximation coefficients. The proposed cancer detection system presents the extracted features to a support vector machine SVM for classification (tumor or non-tumor). To show the robustness of the proposed system, its performance is tested on two public cancer microarray databases.     

Kernel-based learning and feature selection analysis for cancer diagnosis

DNA microarray is a very active area of research in the molecular diagnosis of cancer. Microarray data are composed of many thousands of features and from tens to hundreds of instances, which make the analysis and diagnosis of cancer very complex. In this case, gene/feature selection becomes an elemental and essential task in data classification. In this paper, we propose a complete cancer diagnostic process through kernel-based learning and feature selection. First, support vector machines recursive feature elimination (SVM-RFE) is used to prefilter the genes. Second, the SVM-RFE is enhanced by using binary dragonfly (BDF), which is a recently developed metaheuristic that has never been benchmarked in the context of feature selection. The objective function is the average of classification accuracy rate generated by three kernel-based learning methods. We conducted a series of experiments on six microarray datasets often used in the literature. Experiment results demonstrate that this approach is efficient and provides a higher classification accuracy rate using a reduced number of genes.     

Support vector machines combined with feature selection for breast cancer diagnosis

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the support vector machines (SVM) have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on a SVM-based method combined with feature selection has been proposed. Experiments have been conducted on different training-test partitions of the Wisconsin breast cancer dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, positive and negative predictive values, receiver operating characteristic (ROC) curves and confusion matrix. The results show that the highest classification accuracy (99.51%) is obtained for the SVM model that contains five features, and this is very promising compared to the previously reported results.     

Mammogram classification using contourlet features with forest optimization-based feature selection approach

Multimedia Tools and Applications - Breast cancer continues to be one of the major health issues across the world and it is mostly observed in females. However, the actual cause of this cancer is...     

Gene subset selection in kernel-induced feature space

This paper proposes a new filter approach to gene subset selection for kernel-based classifiers. We derive kernel forms of several well-known class separability criteria, and gene subset selection based on the kernelized criteria is applied to microarray cancer classification problems. The performance of our proposed strategy is compared in experiments with those of the conventional filter approach as well as gene ranking methods.     

基于多尺度和特征融合的肺癌识别方法

针对病人肺结节大小各异、结节征象复杂造成的结节检测困难问题,基于迁移学习提出一种多尺度和特征融合的肺癌识别方法,根据CT图像预测病人未来一年内患肺癌的概率。根据肺结节和肺肿块大小,采用3种不同尺度的图像块输入三维结节检测网络,避免小尺度输入的结节检测网络难以获取大区域病灶整体特征的问题;在多尺度输入基础上采用特征融合策略,将网络提取的瓶颈层特征和输出层特征融合,充分描述病灶的详细特征。在Kaggle Data Science Bowl 2017数据集上的实验结果表明,所提方法降低了肺癌预测的损失值,提高了肺癌识别精度。     

Hybrid PSO feature selection-based association classification approach for breast cancer detection

Neural Computing and Applications - Breast cancer is one of the leading causes of death among women worldwide. Many methods have been proposed for automatic breast cancer diagnosis. One popular...     

基于特征交互与权重集成的癌症分类方法

在癌症分类研究领域,高维、高冗余、类分布不平衡的基因表达数据如何进行特征选择与分类模型构建一直是影响分类准确率的难点。为了提高癌症分类的准确率,提出了基于特征交互与权重集成的癌症分类方法。在特征选择层面,利用多特征对分类信息的增益性交互作用来选出对于标签联合互信息大于单独互信息之和的特征组合,并利用条件互信息选择低冗余的特征,解决基因表达数据的高维、高冗余问题。在分类模型层面,提出结合权重集成反馈机制的二次学习集成模型,综合不同模型对不同类别样本的差异拟合能力,构造不依赖于样本数量的类权重,解决数据类分布不平衡的问题。应用该方法对六种癌症数据进行分类测试,accuracy、sensitivity、precision和F-measure四项指标均稳定在99.39%以上、specificity在94.74%以上,表明该方法能有效提高癌症分类的准确率和稳定性,同时具有对于不同癌症分类的通用性。     

Breast density analysis based on glandular tissue segmentation and mixed feature extraction

Multimedia Tools and Applications - Breast cancer poses a threat to the lives of many women. Breast density is a closely related indicator of breast cancer risk. The aim of this paper is to propose...     

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.     

A feature selection method using improved regularized linear discriminant analysis

Investigation of genes, using data analysis and computer-based methods, has gained widespread attention in solving human cancer classification problem. DNA microarray gene expression datasets are readily utilized for this purpose. In this paper, we propose a feature selection method using improved regularized linear discriminant analysis technique to select important genes, crucial for human cancer classification problem. The experiment is conducted on several DNA microarray gene expression datasets and promising results are obtained when compared with several other existing feature selection methods.     

Mammographic feature enhancement by computerized image processing

Mammographic detection of early breast cancer from X-ray film mammograms requires optimal radiological or image processing techniques. We present an image processing approach based on feature adaptive neighborhood processing with a tunable contrast-enhancement function to enhance mammographic features. This procedure brings out the features in the image with little or no enhancement of the noise. Results show that the proposed technique is intelligently tunable to the requirements of enhancement of specific mammographic features such as microcalcifications, soft-tissue characteristics, etc.     

Automated breast cancer detection by thermography : performance goal and diagnostic feature identification

This paper presents the methodology used for establishing a performance goal and identifying the diagnostic features in a program to develop an automated system for breast cancer detection based on thermographic principles. The receiver operating characteristic (ROC) curve approach is used to evaluate both observer classification and classification rules based on an observer's evaluation of diagnostic features. The multivariate logistic function is applied to two sets of observer evaluated feature sets using 623 normal and 122 breast cancer diagnosed subjects. It is shown that the observer outperforms the multivariate logistic function classifier based on the diagnostic features.     

Enhanced classification loss functions and regularization loss function (ECLFaRLF) algorithm for bowel cancer feature classification

Multimedia Tools and Applications - Bowel cancer is one of the most common cancers as stated in the bowel cancer cases statistics. The proposed technique is to recognize the pattern of tissue...     

Hybrid feature selection by combining filters and wrappers

Feature selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid feature selection method which combines two feature selection methods – the filters and the wrappers. Candidate features are first selected from the original feature set via computationally-efficient filters. The candidate feature set is further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by two bioinformatics problems, namely, protein disordered region prediction and gene selection in microarray cancer data. Experimental results show that equal or better prediction accuracy can be achieved with a smaller feature set. These feature subsets can be obtained in a reasonable time period.     

一种激光诱导荧光光谱特征提取新方法

利用主成分分析（Principal Component Analysis,PCA）和Fisher线性判别分析（Fisher Linear Discriminative Analysis,FLDA）方法相结合提取特征,提出了一种荧光光谱特征提取新方法——PCA_FLDA。实验证明,新方法提高了激光诱导自体荧光光谱对早期结肠癌的诊断精度。对预处理后的240条荧光光谱,利用PCA_FLDA算法提取了50个特征变量,利用支持向量机将其分为正常组织和癌变组织,分类敏感性、特异性和准确率可分别达到97.5%、95.12%和96.25%。     

Soft computing models based feature selection for TRUS prostate cancer image classification

Ultrasound imaging is the most suitable method for early detection of prostate cancer. It is very difficult to distinguish benign and malignant nature of the affliction in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based classification system can provide a second opinion to the radiologists. Generally, objects are described in terms of a set of measurable features in pattern recognition. The selection and quality of the features representing each pattern will have a considerable bearing on the success of subsequent pattern classification. Feature selection is a process of selecting the most wanted or dominating features set from the original features set in order to reduce the cost of data visualization and increasing classification efficiency and accuracy. The region of interest (ROI) is identified from transrectal ultrasound (TRUS) images using DBSCAN clustering with morphological operators after image enhancement using M3-filter. Then the 22 grey level co-occurrence matrix features are extracted from the ROIs. Soft computing model based feature selection algorithms genetic algorithm (GA), ant colony optimization (ACO) and QR are studied. In this paper, QR-ACO (hybridization of rough set based QR and ACO) and GA-ACO (hybridization GA and ACO) are proposed for reducing feature set in order to increase the accuracy and efficiency of the classification with regard to prostate cancer. The selected features may have the best discriminatory power for classifying prostate cancer based on TRUS images. Support vector machine is tailored for evaluation of the proposed feature selection methods through classification. Then, the comparative analysis is performed among these methods. Experimental results show that the proposed method QR-ACO produces significant results. Number of features selected using QR-ACO algorithm is minimal, is successful and has high detection accuracy.     

Cluster analysis of genome-wide expression data for feature extraction

Bio-chip data that consists of high-dimensional attributes have more attributes than specimens. Thus, it is difficult to obtain covariance matrix from tens thousands of genes within a number of samples. Feature selection and extraction is critical to remove noisy features and reduce the dimensionality in microarray analysis. This study aims to fill the gap by developing a data mining framework with a proposed algorithm for cluster analysis of gene expression data, in which coefficient correlation is employed to arrange genes. Indeed, cluster analysis of microarray data can find coherent patterns of gene expression. The output is displayed as table list for convenient survey. We adopt the breast cancer microarray dataset to demonstrate practical viability of this approach.