A fuzzy-genetic approach to breast cancer diagnosis.

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable.     

基于迁移学习和支持向量机的乳腺癌分子分型预测

乳腺癌分子分型对乳腺癌的治疗具有决定性的参考作用,传统的分型方法有创且可能存在假阳性问题,而已有的基于影像学的分型方法准确率较低。本文提出一种利用迁移学习提取特征并结合支持向量机的分型预测方法,对乳腺癌PET/CT标记图像进行融合和归一化,再使用Xception迁移学习网络进行特征提取,最后使用支持向量机进行分类实现分型。对样本测试集进行性能评估表明,Xception+SVM模型的准确率达到0.687,AUC为0.787,优于现有基于影像学的方法,验证了本文方法的有效性。     

基于融合多网络深层卷积特征和稀疏双关系正则化方法的乳腺癌图像分类研究

乳腺癌是全球女性癌症死亡的主要原因之一。现有诊断方法主要是医生通过乳腺癌观察组织病理学图像进行判断,不仅费时费力,而且依赖医生的专业知识和经验,使得诊断效率无法令人满意。针对以上问题,设计基于组织学图像的深度学习框架,以提高乳腺癌诊断准确性,同时减少医生的工作量。开发一个基于多网络特征融合和稀疏双关系正则化学习的分类模型:首先,通过子图像裁剪和颜色增强进行乳腺癌图像预处理;其次,使用深度学习模型中典型的3种深度卷积神经网络(InceptionV3、ResNet-50和VGG-16),提取乳腺癌病理图像的多网络深层卷积特征并进行特征融合;最后,通过利用两种关系(“样本-样本”和“特征-特征”关系)和l_F正则化,提出一种有监督的双关系正则化学习方法进行特征降维,并使用支持向量机将乳腺癌病理图像区分为4类—正常、良性、原位癌和浸润性癌。实验中,通过使用ICIAR2018公共数据集中的400张乳腺癌病理图像进行验证,获得93%的分类准确性。融合多网络深层卷积特征可以有效地捕捉丰富的图像信息,而稀疏双关系正则化学习可以有效降低特征冗余并减少噪声干扰,有效地提高模型的分类性能。     

基于定量影像组学的乳腺肿瘤良恶性诊断

乳腺癌是女性致死率最高的恶性肿瘤之一。为提高诊断效率,提供给医生更加客观和准确的诊断结果。借助影像组学的方法,利用公开数据集BreaKHis中82例患者的乳腺肿瘤病理图像,提取乳腺肿瘤病理图像的灰度特征、Haralick纹理特征、局部二值模式(LBP)特征和Gabor特征共139维影像组学特征,并用主成分分析(PCA)对影像组学特征进行降维,然后利用随机森林(RF)、极限学习机(ELM)、支持向量机(SVM)、k最近邻(kNN)等4种不同的分类器构建乳腺肿瘤良恶性的诊断模型,并对上述不同的特征集进行评估。结果表明,基于支持向量机的影像组学特征的分类效果最好,准确率能达到88.2%,灵敏性达到86.62%,特异性达到89.82%。影像组学方法可为乳腺肿瘤良恶性预测提供一种新型的检测手段,使乳腺肿瘤良恶性临床诊断的准确率得到很大提升。     

基于极限梯度提升和深度神经网络共同决策的心音分类方法

心音是诊断心血管疾病常用的医学信号之一。本文对心音正常/异常的二分类问题进行了研究,提出了一种基于极限梯度提升(XGBoost)和深度神经网络共同决策的心音分类算法,实现了对特征的选择和模型准确率的进一步提升。首先,本文对预处理后的心音信号进行心音分割,在此基础上提取了5个大类的特征,前4类特征采用递归特征消除法进行特征选择,作为XGBoost分类器的输入,最后一类为梅尔频率倒谱系数(MFCC),作为长短时记忆网络(LSTM)的输入。考虑到数据集的不平衡性,本文在两种分类器中皆使用了加权改进的方法。最后采用异质集成决策方法得到预测结果。将本文所提心音分类算法应用于PhysioNet网站在2016年发起的PhysioNet心脏病学挑战赛(CINC)所用公开心音数据库,以测试灵敏度、特异性、修正后的准确率以及F得分,结果分别为93%、89.4%、91.2%、91.3%,通过与其他研究者应用机器学习、卷积神经网络(CNN)等方法的结果比较,在准确率和灵敏度上有明显提高,证明了本文方法能有效地提高心音信号分类的准确性,在部分心血管疾病的临床辅助诊断应用中有很大的潜力。     

基于深度级联森林的乳腺癌基因数据分类研究

乳腺癌基因数据的分类研究在临床医学上具有重要意义。针对基因数据的结构复杂、高维小样本等特点,提出一种最大相关最小条件冗余和深度级联森林结合的基因数据分类方法。选取博德基因研究所乳腺癌基因表达数据集,共98个数据作为样本,每个样本包含1 213个特征基因。首先对数据进行标准化处理,然后利用最大相关最小条件冗余选取特征子集,最后使用深度级联森林对特征子集进行分类。将随机森林、支持向量机和BP神经网络作为对比方法。结果表明,所提出的最大相关最小条件冗余和深度级联森林结合方法的最佳分类准确率达到93.78%,明显优于其他方法。该方法能有效提高乳腺癌基因数据的分类准确率,对基于基因数据的乳腺癌分类具有重要的理论意义与实用价值。     

Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96–100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.     

A hybrid feature selection method for DNA microarray data

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.     

High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4

Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification. To develop a hybrid model using the convolutional neural network (CNN) and the long short-term memory recurrent neural network (LSTM RNN) to classify four benign and four malignant breast cancer subtypes. The proposed CNN-LSTM leveraging on ImageNet uses a transfer learning approach in classifying and predicting four subtypes of each. The proposed model was evaluated on the BreakHis dataset comprises 2480 benign and 5429 malignant cancer images acquired at magnifications of 40×, 100×, 200× and 400×. The proposed hybrid CNN-LSTM model was compared with the existing CNN models used for breast histopathological image classification such as VGG-16, ResNet50, and Inception models. All the models were built using three different optimizers such as adaptive moment estimator (Adam), root mean square propagation (RMSProp), and stochastic gradient descent (SGD) optimizers by varying numbers of epochs. From the results, we noticed that the Adam optimizer was the best optimizer with maximum accuracy and minimum model loss for both the training and validation sets. The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer, and, whereas, 92.5% for multi-class classifier of benign and malignant cancer subtypes, respectively. To conclude, the proposed transfer learning approach outperformed the state-of-the-art machine and deep learning models in classifying benign and malignant cancer subtypes. The proposed method is feasible in classification of other cancers as well as diseases.     

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well.     

基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取

目的乳腺癌的早期发现对患者意义重大。为帮助医生进行乳腺癌的早期检查和诊断,本文提出利用小波分析与图像纹理特征提取相结合的方法来提取乳腺X线图像微钙化点区域,在提高检查准确性的同时避免漏检误检。方法首先利用灰度共生矩阵所提取的能量、熵、对比度、相关性以及小波分解后得到的各层高频系数的方差、能量作为图像的特征向量,然后利用支持向量机进行训练建立最优分类模型。最后利用建立的最优分类模型实现乳腺X线图像微钙化点区域的提取并利用检出率和误检率对结果进行评估。结果使用临床数据进行验证,结果表明利用小波分析与图像纹理特征提取相结合的方法能有效提取乳腺图像中的微钙化点区域。结论基于小波分析和灰度纹理特征的乳腺X线图像微钙化点区域的提取方法比单一的图像纹理特征提取或小波分析等方法,提取的效果更好。另外,该方法设计简单,更易于实现乳腺癌的自动化诊断。     

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

This paper presents a method for breast cancer diagnosis in digital mammogram images. Multi-resolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.     

基于PCA和LDA方法的肿瘤基因表达谱数据分类

目的基因芯片技术对医学临床诊断、治疗、药物开发和筛选等技术的发展具有革命性的影响。针对高维医学数据降维困难及基因表达谱样本数据少、维度高、噪声大的特点,维数约减十分必要。基于主成分分析（principalcomponentanalysis,PCA）和线性判别分析（1ineardiscriminantanalysis,LDA）方法,有效解决了基因表达谱数据分类问题,并提高了识别率。方法分别引人PCA和LDA方法对基因表达谱数据进行降维,然后用K近邻（K—nearestneighbor,KNN）作为分类器对数据进行分类,并分别在乳腺癌和卵巢癌质谱数据上。结果在两类癌症质谱数据上应用PCA和LDA方法能够有效提取分类特征信息,并在保持较高分类正确率的前提下大幅度降低医学数据的维数。结论利用维数约减的方法对癌症基因表达谱数据进行分类,可辅助临床医生发现新的疾病特征,提高疾病诊断的正确率。     

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms

Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew’s correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.     

基于深度学习结合解剖学注意力机制的肺结节良恶性分类

肺结节作为肺癌的初期表现,及时的发现和准确的良恶性诊断对于疾病的治疗具有重要的意义。为了提高肺部CT图像中肺结节良恶性的诊断率,提出一种基于3D ResNet的卷积神经网络,并通过加入解剖学注意力模块有效地提高了肺结节良恶性的分类精度。此外,该方法通过自动分割以获取注意力机制所需的感兴趣区域,实现整个流程的全自动化。解剖学注意力的添加能更好地捕捉图像中的局部纹理信息,进一步提取对于肺结节良恶性诊断有用的特征。本文方法在LIDC-IDRI数据集上进行验证。实验结果表明与传统的3D ResNet及其他现有的方法相比,本文方法在分类精度上有显著的提高,在独立测试集上的最终分类的AUC达到0.973,准确率为0.940。由此可见,本文方法能在辅助医生对肺结节的诊断中起到重要作用。     

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity?=?84%, specificity?=?32.96%, F1 score?=?74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.     

隐结构模型在慢性胃炎辨证中的应用探索——基于EM算法的因子分析方法

名老中医在长年的临床实践中积累了大量的宝贵经验,而这些经验都隐含在众多的临床病历中,机器学习是挖掘出这些隐含经验非常有效的工具,因此利用机器学习技术挖掘出隐含在大量病历资料中的临床经验,对于名老中医经验传承具有非常重要的价值。隐结构模型是张连文教授提出的一种模型,它能够较好地符合中医辨证理论。本文在其方法上进行了一定的简化和改进,并应用于慢性胃炎辨证。主要是采用基于EM（expectation maximum,最大期望）算法的因子分析方法处理病案数据,从而得到慢性胃炎辨证的隐结构,提高了学习速度和模型的准确性。     

Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System

Background: There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.Objectives:This study is aimed at diagnosing TB using hybrid machine learning approaches.Materials and Methods: Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm.Results:Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.     

一种新特征评价方法在红斑鳞状皮肤病诊断中的应用

针对改进F-score特征评价准则没有考虑特征测量量纲对特征重要性的影响,提出一种新的特征重要性评价准则D-score,避免不同特征测量量纲的影响,衡量样本特征在两类或多类之间的辨别能力。将D-score分别与前向顺序搜索、前向顺序浮动搜索两种搜索策略结合,以支持向量机的分类准确率评估所选特征子集的有效性,结合Filter和Wrapper特征选择方法的优势进行特征选择,得到两种混合特征选择方法。将该方法应用于红斑鳞状皮肤病诊断研究,并与基于改进F-score的混合特征选择方法进行了实验对比。十折交叉验证实验结果显示:在红斑鳞状皮肤病诊断研究中,D-score特征评价准则优于改进的F-score准则,基于D-score和前向顺序搜索策略的诊断准确率提高1.11%;D-score结合前向顺序浮动搜索策略的最低诊断准确率提高约3个百分点,平均诊断准确率提高约0.3个百分点,最高诊断准确率达到100%。前向顺序浮动搜索中,D-score准则选择的共有特征是改进F-score准则所选择共有特征的子集。所提出的D-score特征重要性评价准则是一种有效的特征区分能力度量准则,在红斑鳞状皮肤病的诊断中选择出了更有分类意义的特征,提高了诊断准确性。     

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

OBJECTIVE: Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. MATERIAL AND METHODS: A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. RESULTS AND CONCLUSION: Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.