Mass Detection in Digital Mammograms Using Twin Support Vector Machine-Based CAD System

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.

In this paper, a robust marker-controlled watershed method is proposed to yield more accurate segmentation results to delineate the masses in mammograms. The proposed method consists of three main steps: pre-processing, marker extraction and the final segmentation. In the first step, pre-processing algorithm is developed using top-hat morphological filter, wavelet transform followed by a noise smoothing anisotropic diffusion filter. In the second step, a novel technique is developed for the extraction of robust markers to locate the accurate position of the suspicious lesions. Finally, the extracted markers are used within the watershed algorithm to allow the reliable segmentation and quantification of masses in mammograms. The developed computer method was quantitatively evaluated using the area overlap metric (AOM), average minimum Euclidean distance (AMED) and Hausdorff distance (HD). The mean ± standard deviation values of AOM, AMED and HD for our method are 0.83 ± 0.10, 1.49 ± 1.20 mm and 4.62 ± 0.80 mm. We compared our method with previously developed marker-controlled watershed algorithm with respect to the manual segmentation performed by an expert radiologist. Experimental results demonstrate that our method has a strong potential to be used as an aid to radiologists in the interpretation of screening mammograms.

A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms

abstractUniversum, a third class not belonging to either class of the classification problem, allows to incorporate the prior knowledge into the learning process. A lot of previous work have demonstrated that the Universum is helpful to the supervised and semi-supervised classification. Moreover, Universum has already been introduced into the support vector machine (SVM) and twin support vector machine (TSVM) to enhance the generalisation performance. To further increase the generalisation performance, we propose a least squares TSVM with Universum data (-TSVM) in this paper. Our -TSVM possesses the following advantages: first, it exploits Universum data to improve generalisation performance. Besides, it implements the structural risk minimisation principle by adding a regularisation to the objective function. Finally, it costs less computing time by solving two small-sized systems of linear equations instead of a single larger-sized quadratic programming problem. To verify the validity of our proposed algorithm, we conduct various experiments around the size of labelled samples and the number of Universum data on data-sets including seven benchmark data-sets, Toy data, MNIST and Face images. Empirical experiments indicate that Universum contributes to making prediction accuracy improved even stable. Especially when fewer labelled samples given, -TSVM is far superior to the improved LS-TSVM (ILS-TSVM), and slightly superior to the -TSVM.

Glaucoma is a group of eye diseases that cause an irreparable decrease in the view field which is the second most common and leading causes of blindness among retinal diseases. Computer aided diagnosis (CAD) system is an emerging field in medical informatics which has high importance for providing prognosis of diseases. Research efforts have reported with increasing confirmation that the twin support vector machines (TWSVM) have greater accurate diagnosis ability. The goal of TWSVM is to construct two non-parallel planes for each class such that each hyper-plane is closer to one of two classes and as far as possible from the other one. In this paper, we propose a new CAD system for glaucoma diagnosis using TWSVM and three heterogeneous families of feature extraction. In this work, we have used 169 images to classify into normal and glaucoma classes. The performance of the method is evaluated using classification accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curves. The results show that the highest classification accuracy (98,53%) is obtained for the TWSVM using Gaussian kernel function, and this is very promising compared to the SVM results.

We introduce a computer-assisted detection (CAD) system for the automated detection of breast masses in screening mammograms. The system targets the directional behavior of the neighborhood pixels surrounding a reference image pixel. The underlying hypothesis is that in the presence of a mass the directional properties of the breast tissue surrounding the mass should be altered. The hypothesis was tested using a database of 1,337 mammographic regions of interest (ROIs) extracted from DDSM mammograms. There were 681 ROIs containing a biopsy-proven mass centered in the ROI (340 malignant, 341 benign) and 656 ROIs depicting normal breast parenchyma. Initially, eight main directional propagations were identified and modeled given the center of the ROI as the reference pixel. Subsequently, eight novel morphological features were extracted for each direction. The features were designed to characterize the disturbance occurring in normal breast parenchyma due to the presence of a mass. Finally, the extracted features were merged using a back propagation neural network (BPANN). The network served as a non linear classifier trained to determine the presence of a mass centered at the reference image pixel. The BPANN was trained and tested using a leave-one-out sampling scheme. Its performance was evaluated with Receiver Operating Characteristics (ROC) analysis. Our CAD system showed an ROC area index of Az=0.88±0.01 for discriminating mass vs. normal ROIs. Detection performance was robust for both malignant (Az=0.88±0.01) and benign masses (Az=0.87±0.01). Thus, the proposed directional neighborhood analysis (DNA) can be applied effectively to identify suspicious masses in screening mammograms.

Computer-aided diagnosis: Detection of masses on digital mammograms

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aidradiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodologyto detect masses from mammograms. The K-means clustering algorithm is used to split the mammogramsin regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-massregion. SVM is a machine-learning method, based on the principle of structural risk minimization, whichperforms well when applied to data outside the training set. We use a set of textural and shape measures todetect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements.

Weighted Twin Support Vector Machines with Local Information and its application

Recently, Digital mammogram has become one of the most effective techniques for early breast cancer detection. The aim of this study is to develop an automated system for digital mammogram analysis. In the proposed system, the regions of interest (ROIs) in the mammogram are firstly segmented by a topographic representation method called the is contour map. Subsequently, the textural, intensity and shape features are extracted from the ROIs. Then an optimal feature selection method (Correlation-based Feature Selection, CFS) is used to select some important features to classify the ROIs as either masses or non-masses. Finally, we use these selected features to train the cost-sensitive BP neural network. The experimental results show that the proposed method can produce better identification performance than some other algorithms.

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TWSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with, we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

Sparse pinball twin support vector machines

Twin support vector machine (TWSVM) is a classical distance metric learning method for classification problems. The formulation of TWSVM criterion is based on L2-norm distance, which makes TWSVM prone to being influenced by the presence of outliers. In this paper, to develop a robust distance metric learning method, we propose a new objective for TWSVM classifier using L1-norm distance metric, termed as L1-TWSVM. The optimization strategy is to maximize the ratio of the inter-class distance dispersion to the intra-class distance dispersion by using L1-norm distance rather than L2-norm distance. Besides, we design a simple and valid iterative algorithm to solve L1-norm optimal problems, which is easy to actualize and its convergence to an optimum is theoretically ensured. The efficiency and robustness of L1-TWSVM have been validated by experiments on UCI datasets and artificial datasets. The promising experimental results indicate that our proposals outperform relevant state-of-the-art methods in all kinds of experimental settings.