Segmentation of breast MRI using effective Fuzzy C-Means method based on Support Vector Machine

<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>

Intrusion Detection System (IDS) has been a challenging area to be developed in recent years since there are so many kinds of network attacks that are difficult to be classified by system. Moreover, there is always the possibility of the upcoming new attack. Therefore, Intrusion detection system must be improved by new classifiers to obtain the best results not only for classifying attack but also recognizing a new type of attack in an advanced way. These developments will be beneficial for managing security systems in an upcoming era. There are several classifier algorithms for Intrusion Detection System such as Support Vector Machine (SVM) and Fuzzy Kernel C-Means (FKCM). SVM classifier is known as one of the techniques to improve efficiency in attack detection while FKCM is famous because it provides better results to reduce the number of selected data features. Each of classifiers is using kernel trick to improve the classifying matter. In this study, we will compare proposed model which are FKCM with rbf, FKCM with polynomial kernel, SVM with rbf, and SVM with polynomial kernel to find a better result that could increase the accuracy of classifying network attacks. KDD Cup 1999 will be used to evaluate each model. In this study, the use of SVM with polynomial kernel brings the best result with 100 % accuracy can be obtained in 1.9 second.

Aim at the problem of small targets detection in complex infrared background, a method based on the kernel fuzzy clustering and least squares support vector machine (LS-SVM) background predication is proposed. First, partition the training samples by a nearest-neighbor clustering method to get the clustering number and the initial clustering centers. These clustering centers are further processed using kernel fuzzy C-means (KFCM) method. Then, the tuning parameters of the fuzzy model are estimated by LS-SVM. Further these tuning parameters are used to predict the background of infrared images. The prediction image subtracted from the source infrared image gives the residual image. Finally, a threshold selection method based on recursive maximum between-cluster absolute difference is presented to separate the real small target from the residual image. Experimental results are given and they are compared with the results of fuzzy C-means (FCM) detection method. They show that the proposed method has better detection performance.

Fuzzy c-means(FCM)는 퍼지 집합을 응용한 간단하지만 효율적인 클러스터링 방법 중 하나이다. FCM은 여러 응용 분야에서 성공적으로 활용되어 왔지만, 초기화와 잡음에 민감하고 볼록한 형태의 클러스터들만 다룰 수 있는 문제점이 있다. 이 논문에서는 이러한 FCM의 문제점을 해결하기 위해 전역 클러스터링(global clustering) 기법과 커널 클러스터링(kernel clustering) 기법을 결합하여 새로운 비선형 클러스터링 기법인 커널 전역 FCM(kernel global fuzzy c-means, KG-FCM)을 제안한다. 전역 클러스터링은 클러스터링의 초기화를 위한 방법 중 하나로, 순차적으로 클러스터를 하나씩 추가함으로써 초기화에 민감한 FCM의 한계를 극복할 수 있도록 해준다. FCM의 잡음 민감성과 볼록한 클러스터들만 다룰 수 있는 한계를 극복하기 위한 방법은 여러 가지가 있으며 커널 클러스터링이 그 중 하나이다. 커널 클러스터링은 사용하는 커널을 바꿈으로써 쉽게 확장이 가능하므로 이 논문에서는 커널 클러스터링을 사용하였다. 두 방법을 결합함으로써 제안한 방법은 위에서 언급한 문제점들을 해결할 수 있으며, 이는 가상 및 실제 데이터를 이용한 실험 결과를 통해 확인할 수 있다. Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.

국내에 자기공명영상의 보급의 증가와 함께 유방 자기공명영상의 건수도 지속적으로 증가하고 있다. 하지만 이에 비해 자기공명영상 유도하 조직생검술 및 침위치결정술과 같은 유방의 중재적 시술은 초음파 유도하 시술이나 입체정위생검술에 비해 많이 이루어지지 않는다. 유방 자기공명영상은 다른 유방 영상 검사들에 비해 높은 민감도를 보이는 검사법이지만 그 특이도는 제한되는 검사법이기 때문에, 자기공명영상에서만 발견된 병변들은 자기공명영상 유도하 조직생검술이나 자기공명영상 유도하 침위치결정술을 통한 수술적 절제를 통하여 병리적 진단이 이루어져야 한다. 이러한 배경을 바탕으로, 본 종설에서는 자기공명영상 유도하 유방의 중재적 시술들의 적응증, 시술 방법, 시술 과정에서의 여러 고려 사항 및 제한점들에 대해 다루고자 한다.

The high incidence of breast cancer in women has increased significantly in the recent years. Breast MRI involves the use of magnetic resonance imaging to look specifically at the breast. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper utilizes the use of type-II fuzzy sets to enhance the contrast of the breast MRI image. To evaluate the performance of our approach, we run tests over different MRI breast images and show that the overall accuracy offered by the employed approach is high.KeywordsBreast CancerMagnetic Resonance ImagingMembership FunctionSpike Neural NetworkBreast MRIsThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

24 Background: Utilization of breast MRI has increased dramatically in recent years, and there is ongoing debate regarding the role of MRI in patients with breast cancer. Guidelines for MRI use in newly diagnosed breast cancer patients have not been established; therefore, provider ordering of MRI in this population is variable. We investigated patterns of MRI ordering by healthcare providers in the setting of newly diagnosed breast cancer and analyzed predictors of MRI utilization. Methods: All newly diagnosed breast cancer patients presenting for surgical management at a single tertiary care breast center from January 2011 through December 2013 were reviewed. Cases were evaluated for the use of preoperative MRI, and medical specialty of the ordering provider was determined. Patients who presented to a specialized breast center with MRI already completed were compared to those who had MRIs ordered by their treating breast surgeon. Results: A total of 423 women with newly diagnosed breast cancer underwent MRI during the study period. In this group, 253/423 patients (60%) presented to our institution with an MRI already completed. Of MRIs performed prior to presentation, 73% were ordered by a primary care provider, and 27% were ordered by a breast specialist seen previously. Race was a significant predictor of having an MRI before presentation to a breast center (64% of white patients, 41% of black patients, 25% of Asians, and 65% of Hispanic patients, p < .001). Women with commercial insurance were significantly more likely to have an MRI completed before presentation than those with Medicaid (62% versus 37%, p = .002). Age, family history of breast cancer, genetic testing, breast density, mode of diagnosis, and biopsy pathology were not significant factors in determining whether a patient underwent MRI prior to presentation to a breast surgeon. Conclusions: In our experience, the majority of MRIs performed in newly diagnosed patients with breast cancer were ordered by primary care providers as part of their patient’s initial workup. Patient race and insurance status were significant predictors of having an MRI ordered prior to seeing a breast specialist. Further research is needed to develop guidelines for breast MRI use in newly diagnosed cancer patients.

The performance of breast MRI (bMRI) depends on image quality, varying due to patient-related or technical/protocol factors. The resulting artifacts can heavily reduce sensitivity and specificity. We developed a bMRI quality scoring system (bMRI-QUAL) for the bMRI standard protocol. Two independent readers with 3 years of experience evaluated 133 consecutive 1.5-T examinations (sample size calculated to ensure reproducibility). Each sequence was rated on a 4-point scale: 0 = not diagnostic; 1 = relevant artifacts with conserved diagnostic value; 2 = slight artifacts with conserved diagnostic value; 3 = excellent image quality. Each score was weighted in the following formula: (T2-weighted * 1) + (diffusion weighted imaging with b = 0 * 0.5) + (apparent diffusion coefficient maps * 0.5) + (T1-weighted-precontrast * 1) + (T1-weighted-postcontrast * 2) + (T1-weighted-subtracted * 3). This sum was divided by 2.4, obtaining a 0-to-10 global score (GS), with < 6 considered insufficient. The average GS between the two readers was 8.3 ± 1.0, with 102/133 examinations (76.7%) receiving a score ≥ 7 from both readers. Only one case (0.8%) was scored < 6 by both readers. The Bland-Altman analysis showed bias of -0.12, with limits of agreement ranging from -2.52 to +2.29. The difference in GS between the two readers (coverage probability) was 72/133 (54.1%) within ±1 and 123/133 (92.5%) within ±2. Coverage probability within ±1 point ranged from 94.0% to 99.2% across sequences. Average evaluation time/examination was 3 min. bMRI-QUAL is a reproducible quality scoring system. Breast MRI image quality at a tertiary cancer center was good to excellent in approximately 80% of cases. QuestionBreast MRI image quality varies widely and can compromise diagnostic accuracy. A standardized, clinically weighted scoring system is needed to guide repeat-scan decisions and facilitate quality assurance. FindingsIn 133 breast MRIs, the bMRI-QUAL 0-to-10 scoring system proved to be reproducible. Readers assigned a good-to-excellent mean score (8.3) with a 3-min evaluation time. Clinical relevancebMRI-QUAL provides a relatively fast and reproducible evaluation of breast MRI image quality, with each sequence weighted according to its clinical relevance for lesion detection/characterization. It can support decision-making regarding insufficient image quality and facilitate quality assurance protocols.

To identify associations between semiautomatically extracted MRI features and breast cancer molecular subtypes. We analyzed routine clinical pre-operative breast MRIs from 275 breast cancer patients at a single institution in this retrospective, Institutional Review Board-approved study. Six fellowship-trained breast imagers reviewed the MRIs and annotated the cancers. Computer vision algorithms were then used to extract 56 imaging features from the cancers including morphologic, texture, and dynamic features. Surrogate markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor-2 [HER2]) were used to categorize tumors by molecular subtype: ER/PR+, HER2- (luminal A); ER/PR+, HER2+ (luminal B); ER/PR-, HER2+ (HER2); ER/PR/HER2- (basal). A multivariate analysis was used to determine associations between the imaging features and molecular subtype. The imaging features were associated with both luminal A (P = 0.0007) and luminal B (P = 0.0063) molecular subtypes. No association was found for either HER2 (P = 0.2465) or basal (P = 0.1014) molecular subtype and the imaging features. A P-value of 0.0125 (0.05/4) was considered significant. Luminal A and luminal B molecular subtype breast cancer are associated with semiautomatically extracted features from routine contrast enhanced breast MRI.

This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data. Doi: 10.28991/ESJ-2022-06-04-014 Full Text: PDF

In the past two decades, Support Vector Machine (SVM) has become one of the most famous classification techniques. The optimal parameters in an SVM kernel are normally obtained by cross validation, which is a time-consuming process. In this paper, we propose to learn the parameters in an SVM kernel while solving the dual optimization problem. The new optimization problem can be solved iteratively as follows: (a) Fix the parameters in an SVM kernel; solve the variables αi in the dual optimization problem. (b) Fix the variables αi; solve the parameters in an SVM kernel by using the Newton-Raphson method. It can be shown that (a) can be optimized by using standard methods in training the SVM, while (b) can be solved iteratively by using the Newton-Raphson method. Experimental results conducted in this paper show that our proposed technique is feasible in practical pattern recognition applications.

Introduction Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics. Methods In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM). Results Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively. Discussion and conclusion Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

Breast lesion segmentation in magnetic resonance (MR) images is one of the most important parts of clinical diagnostic tools. Pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. On the other hand, unsupervised segmentation methods need no prior knowledge and lead to low performance. However, semi-supervised learning which uses not only a few labeled data, but also a large amount of unlabeled data promises higher accuracy with less effort. In this paper, we propose a new interactive semi-supervised approach to segmentation of suspicious lesions in breast MRI. Using a suitable classifier in this approach has an important role in its performance; in this paper, we present a semi-supervised algorithm improved self-training (IMPST) which is an improved version of self-training method and increase segmentation accuracy. Experimental results show that performance of segmentation in this approach is higher than supervised and unsupervised methods such as K nearest neighbors, Bayesian, Support Vector Machine, and Fuzzy c-Means.

Smoothing approach to alleviate the meager rating problem in collaborative recommender systems

We sought to evaluate the effect of preoperative breast magnetic resonance imaging (BMRI) on the wait time to surgery and to what extent it affects the surgical management plan initially considered. From April 2007 to April 2009, a total of 147 newly diagnosed breast cancer patients who underwent surgery as initial treatment at Marvelle Koffler Breast Centre at Mount Sinai Hospital, Toronto, Ontario, Canada, met the inclusion criteria and were divided into two groups: those who had BMRI (n =71) and those who did not (n = 76). Time to surgery was calculated from the day core biopsy result was available to the date surgery was carried out. Time span between initial surgical consult and the day of the surgery day was also calculated. Change was defined as conversion from conservative to radical surgery and/or unilateral to bilateral procedures. Overall waiting period between histologic diagnosis and treatment was 34.2 days (BMRI group 36.0 days vs. non-BMRI group 32.3 days, P =0.15); and between date surgical management was propose and date of surgery (BMRI group 24.2 days vs. non-BMRI group 22.5 days, P =0.38). Additional workup resulted in seven otherwise occult malignant lesions that required change. Ten percent of patients who underwent BMRI had change in surgical management. The mastectomy rate was higher among those with preoperative BMRI (initial 12% vs. final 26%, P =0.8), but this was not statistically significant. Preoperative BMRI did not delay surgical treatment or correlate with a higher rate of radical treatment.