Diagnosing Breast Cancer Accurately Based on Weighting of Heterogeneous Classification Sub-Models

In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing life expectancy, increasing urbanization and embracing Western lifestyles, the high prevalence of this cancer is noted in the developed world. This paper aims to develop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors to make the right decision in diagnosing breast cancer patients. The proposed model is based on three datasets to develop three sub-models. Each sub-model works independently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patients and this reduces the risk of wrong diagnosing. The model has been developed by conducting intensive experiments. Several classification algorithms were used to select the best one in each sub-model. As the final results, the sub-model accuracies were 72%, 74% and 97%.     

基于贝叶斯方法的乳腺癌预后分析

基于贝叶斯方法研究分析乳腺癌患者的临床病理指标对其预后生存率的影响,并对比直接使用患者阳性淋巴结比率（Lymph Node Ratio,LNR）的局部切检值,以及使用LNR的总体估计值之间的效果差异。采用逻辑回归方法估计患者的总体LNR。之后为了反映各临床病理指标对患者预后的动态影响,基于贝叶斯方法构建动态Cox回归模型进行预后分析,仿真结果表明,使用LNR总体估计值的动态Cox回归模型对数据的拟合效果最好,且该模型相对其他模型而言,对总体生存率的预测准确度最高。     

A Neuro-Fuzzy Approach for Automatic Detection of Breast Cancer Based on Raman Spectroscopy

The harmful presence of cancerous cells in the feminine breast brings as a result, breast cancer, illness that has spread widely lately, not only in Mexico, but in other parts of the planet. In this paper, we present a method of automatic breast cancer classification, in which a Raman signal is classified as coming from a biopsy of healthy tissue （class ω1） or biopsy of diseased tissue （class ω2）; to do so, we created patterns from Raman spectra accurately measuring each Raman peak to provide naturally reduced data to a classifier; we used ANFIS （adaptative neuro-fuzzy inference system） classifier and high rates of correct classification were obtained. This provides the specialists with important clinical tools for a rapid and efficient automatic detection of breast cancer. We consider that our approach can be applicable to other kinds of cancer, e.g., lung, prostate, and stomach.     

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters most. Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification. Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction. Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images. The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.     

基于对象级的高分辨率遥感影像分类研究

依据高分辨率遥感影像的特点,结合深圳市QUICKBIRD数据提出一种基于多尺度分割的对象级遥感分类方法。文中首先利用分形网络演化法(FNEA)进行多尺度图像分割,获取对地表实体更具代表性的图像对象,然后利用对象所包含的光谱、空间特征来确定地物识别中可能要用到的各种特征参数,最后通过构建语义结构实现了研究区地物的逐级分层分类。研究结果表明,本文所采取的方法比传统方法在分类精度上有了明显的提高,为高分辨率遥感影像的信息提取提供了新的技术途径。     

核零空间方法在乳腺癌异常检测中的应用

当今时代,乳腺癌越来越成为了女性的高发病,因此尽早地排除异常因素,进行对症治疗,可以大大降低疾病风险.考虑到乳腺癌数据特征比较多,并且往往不仅存在线性特征还隐含着很多非线性特征,针对这一问题提出利用核零空间算法来进行乳腺癌的异常检测.首先利用核函数将所有的正常样本进行非线性映射变换到高维空间,再通过零空间变换将类内散度...     

用两层分类算法进行视频烟雾检测

为提高视频烟雾检测的准确性,提出一种基于概率的两层最近邻自适应度量分类算法(PTLNN)来进行烟雾检测.该算法以最小化平均绝对误差为原则,结合AdaBoost和KNN算法的优势,充分考虑局部和全局的样本分布,能明显提升分类精度.采用离散余弦变换(DCT)和离散小波变换(DWT)两种方式对烟雾特征进行提取,并验证算法性能.通过与传统算法的对比实验发现,采用离散余弦变换并结合PTLNN算法在视频烟雾检测方面具有更好的效果,既满足实时性要求又提高了检测精度.     

增量式关联分类方法在病毒检测中的应用

传统关联规则挖掘算法主要基于支持度一可信度构架,时空开销的限制使其无法深入挖掘非频繁项集。171前对带类属性的关联分类增量学习研究较少,该文提出一种新的增量式关联分类方法,解决了带类属性数据的增量学习问题,在数据频繁更新时,实现有限时空开销下关联规则的快速提取和维护。实验结果表明,该方法能有效维护并更新关联规则,避免重复学习历史样本,保证分类模型的预测能力。     

Cloud Detection Using Super Pixel Classification and Semantic Segmentation

Journal of Computer Science and Technology - Cloud detection plays a very significant role in remote sensing image processing. This paper introduces a cloud detection method based on super pixel...     

Cervical Cancer Detection Based on Novel Decision Tree Approach

Cervical cancer is a disease that develops in the cervix’s tissue. Cervical cancer mortality is being reduced due to the growth of screening programmers. Cervical cancer screening is a big issue because the majority of cervical cancer screening treatments are invasive. Hence, there is apprehension about standard screening procedures, as well as the time it takes to learn the results. There are different methods for detecting problems in the cervix using Pap (Papanicolaou-stained) test, colposcopy, Computed Tomography (CT), Magnetic Resonance Image (MRI) and ultrasound. To obtain a clear sketch of the infected regions, using a decision tree approach, the captured image has to be segmented and analyzed. The goal of creating a decision tree is to establish prediction model that anticipate the feature vector based on the input variable. This paper deals with investigating various techniques of segmentation for detecting the cervical cancer. It proposes a novel method to develop an assistance system for the detection diagnosis of cervical cancer, based on work of Martin, Byriel and Norup. The analysis is focused on Pap smear pictures of single cells. Smear testing is a method of detecting abnormalities in the blood. Image processing is an effective method for extracting data. It is used to determine the size of cervical carcinoma and the length of the uterus. Martin’s database, which is open source and utilised for analysis and validation, is obtainable for research purposes. Cervical malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the outcomes showed that choice tree is the best classifier indicator with the test dataset. Further investigations ought to be led to improve execution.     

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.     

基于改进分水岭-凹点分割的矿石粒径分级检测方法

为了提高混凝土行业的生产质量,需要对矿石大小做粒径分析,传统方法是采用人工筛分处理,过程中需要耗费大量的人力物力,同时,也存在检测时间长和检测精度低等问题;针对这一难题,通过利用计算机视觉技术,提出了一种基于改进分水岭-凹点分割的矿石粒径分级检测新方法;首先,利用图像自适应中值滤波和改进的多尺度形态学处理,提取矿石轮廓特征;其次,采用改进的分水岭分割和凹点分割相结合,获得矿石之间粘连形成的深凹点集合;最后,引入反向链码模板对凹点集进行有效的分离,从而对矿石粒径做出精准的统计分析;实验结果表明,该算法的粒径分级与人工筛分的粒径分级相比较,两者之间的累积误差率在5%以内,具有较高的准确性与实用性,值得大力的推广与应用。     

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).     

基于深度学习的乳腺癌病理图像分类研究综述

准确、高效的乳腺癌病理图像分类是计算机辅助诊断的重要研究内容之一。随着机器学习技术的发展,深度学习日渐成为一种有效的乳腺癌病理图像分类处理方法。分析了乳腺癌病理图像分类方法及目前存在的问题;介绍了四种相关的深度学习模型,对基于深度学习的乳腺癌病理图像分类方法进行梳理,并通过实验对比分析现有模型的性能;最后对乳腺癌病理图像分类的关键问题进行了总结,并讨论了未来研究的发展趋势。     

基于边缘检测的多类别医学图像分类方法

针对乳腺X光医学图像多类分类精度普遍较低的问题,提出了一种基于边缘检测的医学图像多类分类新方法。首先对乳腺X光医学图像进行预处理包括图像去噪和图像增强,再通过边缘检测方法,获取乳腺X光医学图像中的肿块区域,对检测到的肿块区域使用灰度共生矩阵提取特征,对于提取到的特征,采用支持向量机（Support vector machine,SVM）的方法进行分类;对于检测不到肿块区域的乳腺X光医学图像可直接分类为无乳腺癌（即正常）类。实验结果表明,与传统的支持向量机多类分类算法相比,基于边缘检测的医学图像多类分类新方法在乳腺X光医学图像上具有更高的分类精度。     

Efficient Object Detection and Classification Approach Using HTYOLOV4 and M²RFO-CNN

Object detection and classification are the trending research topics in the field of computer vision because of their applications like visual surveillance. However, the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic environment with high accuracy and precision. The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modified Manta-Ray Foraging Optimization-based Convolution Neural Network. Initially, in the pre-processing, the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve the Algorithm method for image resizing and noise removal. The noiseless resized image sequences contrast was enhanced using Contrast Limited Adaptive Edge Preserving Algorithm. And, with the contrast-enhanced image sequences, the Hyperbolic Tangent based You Only Look Once V4 was trained for object detection. Additionally, to detect smaller objects with high accuracy, Grasp configuration was observed for every detected object. Finally, the Modified Manta-Ray Foraging Optimization-based Convolution Neural Network method was carried out for the detection and the classification of objects. Comparative experiments were conducted on various benchmark datasets and methods that showed improved accurate detection and classification results.     

应用突函数差异算法进行癌症分类的基因选择(英文)

研究了有关癌症分类的基因选择问题。开发了集成的基于平滑剪切绝对偏差罚分的SVM—特征选择方法,直接最小化分类器的性能。为解决优化问题,应用了突函数差异算法(difference of convex functionsal-gorithms,DCA)这一进行非突连续优化的通用框架,致使连续线性规划算法有限收敛。真实数据集上的先验实验表明算法达到了预想目标:在压缩大量属性的同时,保持了较小分类差错。     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

利用主元方法进行传感器故障检测的行为分析

主元分析方法(PCA)是基于多元统计分析的过程监测和故障诊断手段。在假设过程只存在传感器故障的情况下，系统地分析了PCA方法在传感器典型故障下的检测行为。首先导出了Hoteuing T^2和Q两个检测统计量在传感器不同故障下的变化关系和规律，然后从理论上给出了每个传感器故障的可检测性条件。最后通过火电厂锅炉过程中传感器故障检测实例验证了所得到的结论。     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.