Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

A hybrid classifier committee for analysing asymmetry features in breast thermograms

Breast cancer is the most commonly occurring form of cancer in women. While mammography is the standard modality for diagnosis, thermal imaging provides an interesting alternative as it can identify tumors of smaller size and hence lead to earlier detection. In this paper, we present an approach to analysing breast thermograms based on image features and a hybrid multiple classifier system. The employed image features provide indications of asymmetry between left and right breast regions that are encountered when a tumor is locally recruiting blood vessels on one side, leading to a change in the captured temperature distribution. The presented multiple classifier system is based on a hybridisation of three computational intelligence techniques: neural networks or support vector machines as base classifiers, a neural fuser to combine the individual classifiers, and a fuzzy measure for assessing the diversity of the ensemble and removal of individual classifiers from the ensemble. In addition, we address the problem of class imbalance that often occurs in medical data analysis, by training base classifiers on balanced object subspaces. Our experimental evaluation, on a large dataset of about 150 breast thermograms, convincingly shows our approach not only to provide excellent classification accuracy and sensitivity but also to outperform both canonical classification approaches as well as other classifier ensembles designed for imbalanced datasets.     

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.     

Intelligent breast cancer recognition using particle swarm optimization and support vector machines

Breast cancer is the most common cancer among women, except for skin cancer, but early detection of breast cancer improves the chances of survivability. Data mining is widely used for this purpose. As technology develops, large number of breast tumour features are being collected. Using all these features for cancer recognition is expensive and time-consuming. Feature extraction is necessary for increasing the classification accuracy. The goal of this work is to recognise breast cancer using extracted features. To reach this goal, a combination of clustering and classification is used. Particle swarm optimization is used to recognise tumour patterns. The membership degree of each tumour to the patterns is calculated and considered as a new feature. Support vector machine is then employed to classify tumours. Finally this method is analysed in terms of its accuracy, specificity, sensitivity and CPU time consuming using Wisconsin Diagnostic Breast Cancer data set.     

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.     

Automatic nipple detection in breast thermograms

Breast cancer is one of the most dangerous diseases for women. Detecting breast cancer in its early stage may lead to a reduction in mortality. Although the study of mammographies is the most common method to detect breast cancer, it is outperformed by the analysis of thermographies in dense tissue (breasts of young women). In the last two decades, several computer-aided diagnosis (CAD) systems for the early detection of breast cancer have been proposed. Breast cancer CAD systems consist of many steps, such as segmentation of the region of interest, feature extraction, classification and nipple detection. Indeed, the nipple is an important anatomical landmark in thermograms. The location of the nipple is invaluable in the analysis of medical images because it can be used in several applications, such as image registration and modality fusion. This paper proposes an unsupervised, automatic, accurate, simple and fast method to detect nipples in thermograms. The main stages of the proposed method are: human body segmentation, determination of nipple candidates using adaptive thresholding and detection of the nipples using a novel selection algorithm. Experiments have been carried out on a thermograms dataset to validate the proposed method, achieving accurate nipple detection results in real-time. We also show an application of the proposed method, breast cancer classification in dynamic images, where the new nipple detection technique is used to segment the region of the two breasts from the infrared image. A dataset of dynamic thermograms has been used to validate this application, achieving good results.     

Detection and differentiation of breast cancer using neural classifiers with first warning thermal sensors

Breast cancer occurs when cells in the breast begin to grow out of control and invade nearby tissues or spread throughout the body. It is one of the leading causes of death in women. Cancer development appears to generate an increase in the temperature on the breast surface. The limitations of mammography as a screening modality, especially in young women with dense breasts, necessitated the development of novel and more effective screening strategies with high sensitivity and specificity. The aim of this study was to evaluate the feasibility of discrete thermal data (DTD) as a potential tool for the early detection of the breast cancer.Our protocol uses 1170, 16-sensor data collected from 54 individuals consisting of three different kinds of breast conditions: namely, normal, benign and cancerous breast. We compared two different kinds of neural network classifiers: the feedforward neural network and the radial basis function classifier. Temperature data from the 16 temperature sensors on the surface of the two breasts (eight sensors on each side) are fed as input to the classifiers. We demonstrated a sensitivity of 84% and 91% for these classifiers (feedforward and radial basis function, respectively) with a specificity of 100%. Our classifying systems are ready to run on large data sets.     

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%.     

基于多特征描述的乳腺癌肿瘤病理自动分级

为了辅助病理医生快速高效诊断乳腺癌并提供乳腺癌预后信息,提出一种计算机辅助乳腺癌肿瘤病理自动分级方法。该方法使用深度卷积神经网络和滑动窗口自动检测病理图像中的细胞;随后综合运用基于稀疏非负矩阵分解的颜色分离、前景标记的分水岭算法以及椭圆拟合得到每个细胞的轮廓。基于检测到的细胞和拟合出的细胞轮廓,提取出肿瘤的组织结构特征和上皮细胞的纹理形状特征等共203维的特征,运用这些特征训练支持向量机分类器(SVM),实现对病理组织图像自动分级。17位患者的49张H&E染色的乳腺癌病理组织图像自动分级的100次十折交叉检验评估结果表明:基于病理图像的细胞形状特征与组织的空间结构特征对病理图像的高、中、低分化等级分类整体准确率为90.20%;同时对高、中、低各分化等级的区分准确率分别为92.87%、82.88%、93.61%。相比使用单一结构特征或者纹理特征的方法,所提方法具有更高的准确率,能准确地对病理组织图像中肿瘤的高级和低级分化程度自动分级,且各分级之间的准确率差异较小。     

A review of computer assisted detection/diagnosis (CAD) in breast thermography for breast cancer detection

Breast cancer is the leading type of cancer diagnosed in women. For years human limitations in interpreting the thermograms possessed a considerable challenge, but with the introduction of computer assisted detection/diagnosis (CAD), this problem has been addressed. This review paper compares different approaches based on neural networks and fuzzy systems which have been implemented in different CAD designs. The greatest improvement in CAD systems was achieved with a combination of fuzzy logic and artificial neural networks in the form of FALCON-AART complementary learning fuzzy neural network (CLFNN). With a CAD design based on FALCON-AART, it was possible to achieve an overall accuracy of near 90%. This confirms that CAD systems are indeed a valuable addition to the efforts for the diagnosis of breast cancer. Lower cost and high performance of new infrared systems combined with accurate CAD designs can promote the use of thermography in many breast cancer centres worldwide.     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

Probabilistic neural network for breast cancer classification

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.     

Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.     

Detection and Classification of Hemorrhages in Retinal Images

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy (DR). Hemorrhages is the first clinically visible symptoms of DR. This paper presents a new technique to extract and classify the hemorrhages in fundus images. The normal objects such as blood vessels, fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages. For masking blood vessels, thresholding that separates blood vessels and background intensity followed by a new filter to extract the border of vessels based on orientations of vessels are used. For masking optic disc, the image is divided into sub-images then the brightest window with maximum variance in intensity is selected. Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques. Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features. Three different types of Support Vector Machine (SVM), Linear SVM, Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemorrhages or healthy. The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico. The performance of the method is measured based on average sensitivity, specificity, F-score and accuracy. Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.     

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms.     

Bi-modal breast cancer classification system

Breast cancer is known as one of the major causes of mortality among women. Breast cancer can be treated with better patient outcomes and significantly lower costs if it is detected early. Digital mammograms are the type of medical images most often used, and which are the most reliable, for the detection of breast cancer. The presence of microcalcification clusters in mammograms contributes to evidence for the detection of early stages of cancer. In this paper, a bi-modal artificial neural network (ANN) based breast cancer classification system is proposed. The microcalcifications are extracted with adaptive neural networks that are trained with cancer/malignant and normal/benign breast digital mammograms of both cranio caudal (CC) and medio-latral oblique (MLO) views. The performance of the networks is evaluated using receiver operating characteristic (ROC) curve analysis. Sensitivity–specificity of 98.0–100.0 for the CC view and 96.0–100.0 for the MLO view networks are recorded for 200 unseen digital database for screening mammography (DDSM) cases. The DDSM database, developed at the University of South Florida, is a resource for use by the mammographic image analysis research community. The OR logic is then used to fuse individual networks to get a best sensitivity–specificity of 100.0–100.0 for the ensemble. However, the overall sensitivity–specificity of the ANN ensemble is somewhat degraded at the expense of a robust or sensitive system, i.e., the probability to miss out a true positive case is minimized.     

A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimension-related feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure. The results were evaluated by expert radiologists and are very promising, showing equal or higher success rates compared to other related works, despite the fact that some of them used only selected portions of this specific mammographic database. In contrast, our methodology is applied to the complete miniMIAS database and it exhibits the reliability that is normally required for clinical use in CAD systems.