A New Method to Classify Breast Cancer Tumors and Their Fractionation

http://dx.doi.org/10.5902/2179460X19428In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses.In this paper, suspicious breast tumors were classified by using the neural network and the growth area method has been used for a fractionation of the benign or malignant areas of the normal tissue. Features extracted from input tissues are including statistical features and characteristics of spatial dependence. The advantage of this method is using of phase adaptive threshold based on entropy which leads to more accurate extraction of tumors and also corresponded with the nature of mammogram images. As a result, this method mimics of the human eye operation to detect abnormal masses. Database used in this paper is the MIAS mammogram database including 238 normal, benign and malignant mammograms. The accuracy obtained with 38 features is equal to 86.66% for detecting abnormal masses and 38.05 % for normal masses

Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering

Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets

Possibilistic and fuzzy clustering methods for robust analysis of non-precise data

This work focuses on robust clustering of data affected by imprecision. The imprecision is managed in terms of fuzzy sets. The clustering process is based on the fuzzy and possibilistic approaches. In both approaches the observations are assigned to the clusters by means of membership degrees. In fuzzy clustering the membership degrees express the degrees of sharing of the observations to the clusters. In contrast, in possibilistic clustering the membership degrees are degrees of typicality. These two sources of information are complementary because the former helps to discover the best fuzzy partition of the observations while the latter reflects how well the observations are described by the centroids and, therefore, is helpful to identify outliers. First, a fully possibilistic k-means clustering procedure is suggested. Then, in order to exploit the benefits of both the approaches, a joint possibilistic and fuzzy clustering method for fuzzy data is proposed. A selection procedure for choosing the parameters of the new clustering method is introduced. The effectiveness of the proposal is investigated by means of simulated and real-life data

Kernel-based modified fuzzy possibilistic c-means clustering

Classification and clustering algorithms are, without doubt, a useful tool to explore data structures, and have been widely employed in many domains such as pattern recognition, image processing, data mining, and data analysis. The focus of this paper is the partitioning problem with a special interest in kernel method. The aim of this paper is to extend this method to the modified fuzzy possibilistic c-means (MFPCM) algorithm. It is realized by substitution of a kernel-induced distance metric for the Euclidean distance, and the corresponding algorithm is called kernel MFPCM algorithm. Numerical simulations are given to illustrate the performances of the proposed method