Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

Computer-aided detection and classification of microcalcifications in mammograms: a survey

Breast cancer continues to be a significant public health problem in the world. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year in the United States. Even more disturbing is the fact that one out of eight women in US will develop breast cancer at some point during her lifetime. Primary prevention seems impossible since the causes of this disease still remain unknown. Early detection is the key to improving breast cancer prognosis. Mammography is one of the reliable methods for early detection of breast carcinomas. There are some limitations of human observers, and it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. The presence of microcalcification clusters (MCCs) is an important sign for the detection of early breast carcinoma. An early sign of 30–50% of breast cancer detected mammographically is the appearance of clusters of fine, granular microcalcification, and 60–80% of breast carcinomas reveal MCCs upon histological examinations. The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control. In this survey paper, we summarize and compare the methods used in various stages of the computer-aided detection systems (CAD). In particular, the enhancement and segmentation algorithms, mammographic features, classifiers and their performances are studied and compared. Remaining challenges and future research directions are also discussed.     

Approaches for automated detection and classification of masses in mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The estimated sensitivity of radiologists in breast cancer screening is only about 75%, but the performance would be improved if they were prompted with the possible locations of abnormalities. Breast cancer CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This paper discusses the methods for mass detection and classification, and compares their advantages and drawbacks.     

Classification based on fuzzy robust PCA algorithms and similarity classifier

In this article, classification method is proposed where data is first preprocessed using fuzzy robust principal component analysis (FRPCA) algorithms to obtain data in a more feasible form. After this we use similarity classifier for the classification. We tested this procedure for breast cancer data and liver-disorder data. The results were quite promising and better classification accuracy was achieved than using traditional PCA and similarity classifier. Fuzzy robust principal component analysis algorithms seem to have the effect that they project these data sets in a more feasible form, and together with similarity classifier classification on accuracy of 70.25% was achieved with liver-disorder data and 98.19% accuracy was achieved with breast cancer data. Compared to the results achieved with traditional PCA and similarity classifier about 4% higher accuracy was achieved with liver-disorder data and about 0.5% higher accuracy was achieved with breast cancer data.     

Breast cancer classification using deep belief networks

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions.     

融合空间和通道特征的高精度乳腺癌分类方法

组织病理学图像是鉴别乳腺癌的黄金标准,所以对乳腺癌组织病理学图像的自动、精确的分类具有重要的临床应用价值。为了提高乳腺组织病理图像的分类准确率,从而满足临床应用的需求,提出了一种融合空间和通道特征的高精度乳腺癌分类方法。该方法使用颜色归一化来处理病理图像并使用数据增强扩充数据集,基于卷积神经网络（CNN）模型DenseNet和压缩和激励网络（SENet）融合病理图像的空间特征信息和通道特征信息,并根据压缩-激励（SE）模块的插入位置和数量,设计了三种不同的BCSCNet模型,分别为BCSCNetⅠ、BCSCNetⅡ、BCSCNetⅢ。在乳腺癌癌组织病理图像数据集（BreaKHis）上展开实验。通过实验对比,先是验证了对图像进行颜色归一化和数据增强能提高乳腺的分类准确率,然后发现所设计的三种乳腺癌分类模型中精度最高为BCSCNetⅢ。实验结果表明,BCSCNetⅢ的二分类准确率在99.05%~99.89%,比乳腺癌组织病理学图像分类网络（BHCNet）提升了0.42个百分点;其多分类的准确率在93.06%~95.72%,比BHCNet提升了2.41个百分点。证明了BCSCNet能准确地对乳腺癌组织病理图像进行分类,同时也为计算机辅助乳腺癌诊断提供了可靠的理论支撑。     

基于联合训练的分类器的乳腺癌图像分类

利用机器学习的乳腺癌组织病理图像诊断节省了大量的人力物力,因此提高乳腺癌组织病理图像识别准确率有很好的现实意义;针对单一分类器和集成学习分类器模型观测域有限容易陷入局部最优的问题,提出一种基于联合训练的分类器模型;通过单一分类器相互影响扩大观测感知域来寻找损失最小的估计点,根据估计点来迭代优化超参数进而联合训练出拟合性能最好的分类器,这样既汲取不同分类器模型的可取之处来增强泛化能力,又加大了模型观测域在可以更快的得到全局最优的同时提升了识别准确率;实验表明,提出的联合训练的分类器能够提升乳腺癌组织病理学图像的分类性能,在不同放大倍数40×、100×、200×、400×下图像良恶性分类准确率分别为99.67%、98.08%、99.01%、96.34%。     

Statistical analysis of mammographic features and its classification using support vector machine

This study aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.     

基于主要特征抽取的重现概念漂移处理算法

针对重现概念漂移检测中的概念表征和分类器选择问题,提出了一种适用于含重现概念漂移的数据流分类的算法——基于主要特征抽取的概念聚类和预测算法（Conceptual clustering and prediction through main feature extraction, MFCCP）。MFCCP通过计算不同批次样本的主要特征及影响因子的差异度以识别重复出现的概念,为每个概念维持且及时更新一个分类器,并依据Hoeffding不等式选择最合适的分类器对当前样本集实施分类,以 提高对概念漂移的反应能力。在3个数据集上的实验表明：MFCCP在含重现概念漂移的数据集上的分类准确率,对概念漂移的反应能力及对概念漂移检测的准确率均明显优于其他4种 对比算法,且MFCCP也适用于对不含重现概念漂移的数据流进行分类。     

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.     

Pneumonia classification using quaternion deep learning

Pneumonia is an infection in one or both the lungs because of virus or bacteria through breathing air. It inflames air sacs in lungs which fill with fluid which further leads to problems in respiration. Pneumonia is interpreted by radiologists by observing abnormality in lungs in case of fluid in Chest X-Rays. Computer Aided Detection Diagnosis (CAD) tools can assist radiologists by improving their diagnostic accuracy. Such CAD tools use neural networks which are trained on Chest X-Ray dataset to classify a Chest X-Ray into normal or infected with Pneumonia. Convolution neural networks have shown remarkable performance in object detection in an image. Quaternion Convolution neural network (QCNN) is a generalization of conventional convolution neural networks. QCNN treats all three channels (R, G, B) of color image as a single unit and it extracts better representative features and which further improves classification. In this paper, we have trained Quaternion residual network on a publicly available large Chest X-Ray dataset on Kaggle repository and obtained classification accuracy of 93.75% and F-score of .94. We have also compared our performance with other CNN architectures. We found that classification accuracy was higher with Quaternion Residual network when we compared it with a real valued Residual network.

     

Breast cancer image assessment using an adaptative network-based fuzzy inference system

In Brazil breast cancer is the foremost cause of fatality by cancer for women. Given that the causes are unidentified, it cannot be prevented. Mammography is one of the most reliable exams for breast cancer detection and it is based on image analysis by radiologists. Early detection is the key issue for breast cancer control and computer-aided diagnosis system can help ra diologists in detection and diagnosing breast abnormalities. Hybrid neuro-fuzzy systems are suitable for pattern recognition tasks and therefore useful for medical diagnosis support through pattern identification in mammographic images. This study presents an Adaptative Network-based Fuzzy Inference System (ANFIS) that classifies the mammographic images calcification region of interest as benign or malign and provides an important tool for breast cancer image assessment. The ANFIS model, utilized in the mammogram region of interest’s classification phase, reached a maximum accuracy rate of 99.75%.     

Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC) that is standard data-set used in CAD Systems for Lungs Nodule classification. The results obtained from proposed CAD system are good as compare to previous CAD systems. The sensitivity of 96.31% is achieved in the proposed CAD system.     

A two-stage mechanism for registration and classification of ECG using Gaussian mixture model

An automatic classifier for electrocardiogram (ECG) based cardiac abnormality detection using Gaussian mixture model (GMM) is presented here. In first stage, pre-processing that includes re-sampling, QRS detection, linear prediction (LP) model estimation, residual error signal computation and principal component analysis (PCA) has been used for registration of linearly independent ECG features. GMM is here used for classification based on the registered features in a two-class pattern classification problem using 730 ECG segments from MIT-BIH Arrhythmia and European ST-T Ischemia datasets. A set of 12 features explaining 99.7% of the data variability is obtained using PCA from residual error signals for GMM based classification. Sixty percent of the data is used for training the classifier and 40% for validating. It is observed that the overall accuracy of the proposed strategy is 94.29%. As an advantage, it is also verified that Chernoff bound and Bhattacharya bounds lead to minimum error for GMM based classifier. In addition, a comparative study is done with the standard classification techniques with respect to its overall accuracy.     

网络背景流量的分类与识别研究综述

互联网流量分类是识别网络应用和分类相应流量的过程,这被认为是现代网络管理和安全系统中最基本的功能。与应用相关的流量分类是网络安全的基础技术。传统的流量分类方法包括基于端口的预测方法和基于有效载荷的深度检测方法。在目前的网络环境下,传统的方法存在一些实际问题,如动态端口和加密应用,因此采用基于流量统计特征的机器学习（ML）技术来进行流量分类识别。机器学习可以利用提供的流量数据进行集中自动搜索,并描述有用的结构模式,这有助于智能地进行流量分类。起初使用朴素贝叶斯方法进行网络流量分类的识别和分类,对特定流量进行实验时,表现较好,准确度可达90%以上,但对点对点传输网络流量（P2P）等流量识别准确度仅能达到50%左右。然后有使用支持向量机（SVM）和神经网络（NN）等方法,神经网络方法使整体网络流量的分类准确度能达到80%以上。多项研究结果表明,对于多种机器学习方法的使用和后续的改进,很好地提高了流量分类的准确性。     

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field. In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields. This helps to show better results especially for diagnosing diseases. Of late the early prediction of cancer is necessary to prevent disease-causing problems. This work is proposed to identify lung cancer using lung computed tomography (CT) scan images. It helps to identify cancer cells’ affected areas. In the present work, the original input image from Lung Image Database Consortium (LIDC) typically suffers from noise problems. To overcome this, the Gabor filter used for image processing is highly enhanced. In the next stage, the Spherical Iterative Refinement Clustering (SIRC) algorithm identifies cancer-suspected areas on the CT scan image. This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages. These new methods help to remove unwanted portions of the CT image and better utilization the image. The subspace extraction of features approach is beneficial for evaluating lung cancer. This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures (CCPNN). By using the feature values from the fourth step of the procedure, the proposed CCPNN tends to categorize the lesion in the lung nodular site. The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall (%), precision (%), F-measure (percent), and accuracy (%). Finally, the incorrect classification ratios are determined to compare the trained networks’ effectiveness, through these parameters of CCPNN, it obtains the outstanding performance of 98.06% and it has provided the lowest false ratio of 1.8%.     

A GAs based approach for mining breast cancer pattern

Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.     

Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis

In this paper, we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms was modeled after expert observers. In this work, we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help to improve its classification accuracy, thereby leading to an improved “second opinion” to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.     

Automated assessment of breast tissue density in digital mammograms

Mammographic density is known to be an important indicator of breast cancer risk. Classification of mammographic density based on statistical features has been investigated previously. However, in those approaches the entire breast including the pectoral muscle has been processed to extract features. In this approach the region of interest is restricted to the breast tissue alone eliminating the artifacts, background and the pectoral muscle. The mammogram images used in this study are from the Mini-MIAS digital database. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Gray level thresholding and connected component labeling is used to eliminate the artifacts and pectoral muscles from the region of interest. Statistical features are extracted from this region which signify the important texture features of breast tissue. These features are fed to the support vector machine (SVM) classifier to classify it into any of the three classes namely fatty, glandular and dense tissue.The classifier accuracy obtained is 95.44%.     

Implementing automated diagnostic systems for breast cancer detection

This paper intends to an integrated view of implementing automated diagnostic systems for breast cancer detection. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of breast cancer. Because of the importance of making the right decision, better classification procedures for breast cancer have been searched. The classification accuracies of different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine (SVM), which were trained on the attributes of each record in the Wisconsin breast cancer database, were compared. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. This research demonstrated that the SVM achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.