Design of Machine Learning Algorithms with Applications to Breast Cancer Detection

Machine learning is concerned with the design and development of algorithms and techniques that allow computers to 'learn' from experience with respect to some class of tasks and performance measure. One application of machine learning is to improve the accuracy and efficiency of computer-aided diagnosis systems to assist physician, radiologists, cardiologists, neuroscientists, and health-care technologists. This thesis focuses on machine learning and the applications to breast cancer detection. Emphasis is laid on preprocessing of features, pattern classification, and model selection. Before the classification task, feature selection and feature transformation may be performed to reduce the dimensionality of the features and to improve the classification performance. Genetic algorithm (GA) can be employed for feature selection based on different measures of data separability or the estimated risk of a chosen classifier. A separate nonlinear transformation can be performed by applying kernel principal component analysis and kernel partial least squares. Different classifiers are proposed in this work: The SOM-RBF network combines self-organizing maps (SOMs) and radial basis function (RBF) networks, with the RBF centers set as the weight vectors of neurons from the competitive layer of a trained SaM. The pairwise Rayleigh quotient (PRQ) classifier seeks one discriminating boundary by maximizing an unconstrained optimization objective, named as the PRQ criterion, formed with a set of pairwise const~aints instead of individual training samples. The strict 2-surface proximal (S2SP) classifier seeks two proximal planes that are not necessary parallel to fit the distribution of the samples in the original feature space or a kernel-defined feature space, by ma-ximizing two strict optimization objectives with a 'square of sum' optimization factor. Two variations of the support vector data description (SVDD) with negative samples (NSVDD) are proposed by involving different forms of slack vectors, which learn a closed spherically shaped boundary, named as the supervised compact hypersphere (SCH), around a set of samples in the target class. \Ve extend the NSVDDs to solve the multi-class classification problems based on distances between the samples and the centers of the learned SCHs in a kernel-defined feature space, using a combination of linear discriminant analysis and the nearest-neighbor rule. The problem of model selection is studied to pick the best values of the hyperparameters for a parametric classifier. To choose the optimal kernel or regularization parameters of a classifier, we investigate different criteria, such as the validation error estimate and the leave-out-out bound, as well as different optimization methods, such as grid search, gradient descent, and GA. By viewing the tuning problem of the multiple parameters of an 2-norm support vector machine (SVM) as an identification problem of a nonlinear dynamic system, we design a tuning system by employing the extended Kalman filter based on cross validation. Independent kernel optimization based on different measures of data separability are a~so investigated for different kernel-based classifiers. Numerous computer experiments using the benchmark datasets verify the theoretical results, make comparisons among the techniques in measures of classification accuracy or area under the receiver operating characteristics curve. Computational requirements, such as the computing time and the number of hyper-parameters, are also discussed. All of the presented methods are applied to breast cancer detection from fine-needle aspiration and in mammograms, as well as screening of knee-joint vibroarthrographic signals and automatic monitoring of roller bearings with vibration signals. Experimental results demonstrate the excellence of these methods with improved classification performance. For breast cancer detection, instead of only providing a binary diagnostic decision of 'malignant' or 'benign', we propose methods to assign a measure of confidence of malignancy to an individual mass, by calculating probabilities of being benign and malignant with a single classifier or a set of classifiers

Development of Features and Feature Reduction Techniques for Mammogram Classification

Breast cancer is one of the most widely recognized reasons for increased death rate among women. For reduction of the death rate due to breast cancer, early detection and treatment are of utmost necessity. Recent developments in digital mammography imaging systems have aimed to better diagnosis of abnormalities present in the breast. In the current scenario, mammography is an effectual and reliable method for an accurate detection of breast cancer. Digital mammograms are computerized X-ray images of breasts. Reading of mammograms is a crucial task for radiologists as they suggest patients for biopsy. It has been studied that radiologists report several interpretations for the same mammographic image. Thus, mammogram interpretation is a repetitive task that requires maximum attention for the avoidance of misinterpretation. Therefore, at present, Computer-Aided Diagnosis (CAD) system is exceptionally popular which analyzes the mammograms with the usage of image processing and pattern recognition techniques and classify them into several classes namely, malignant, benign, and normal. The CAD system recognizes the type of tissues automatically by collecting and analyzing significant features from mammographic images. In this thesis, the contributions aim at developing the new and useful features from mammograms for classification of the pattern of tissues. Additionally, some feature reduction techniques have been proposed to select the reduced set of significant features prior to classification. In this context, five different schemes have been proposed for extraction and selection of relevant features for subsequent classification. Using the relevant features, several classifiers are employed for classification of mammograms to derive an overall inference. Each scheme has been validated using two standard databases, namely MIAS and DDSM in isolation. The achieved results are very promising with respect to classification accuracy in comparison to the existing schemes and have been elaborated in each chapter. In Chapter 2, hybrid features are developed using Two-Dimensional Discrete Wavelet Transform (2D-DWT) and Gray-Level Co-occurrence Matrix (GLCM) in succession. Subsequently relevant features are selected using t-test. The resultant feature set is of substantially lower dimension. On application of various classifiers it is observed that Back-Propagation Neural Network (BPNN) gives better classification accuracy as compared to others. In Chapter 3, a Segmentation-based Fractal Texture Analysis (SFTA) is used to extract the texture features from the mammograms. A Fast Correlation-Based Filter (FCBF) method has been used to generate a significant feature subset. Among all classifiers, Support Vector Machine (SVM) results superior classification accuracy. In Chapter 4, Two-Dimensional Discrete Orthonormal S-Transform (2D-DOST) is used to extract the features from mammograms. A feature selection methodology based on null-hypothesis with statistical two-sample t-test method has been suggested to select most significant features. This feature with AdaBoost and Random Forest (AdaBoost-RF) classifier outperforms other classifierswith respect to accuracy. In Chapter 5, features are derived using Two-Dimensional Slantlet Transform (2D-SLT) from mammographic images. The most significant features are selected by utilizing the Bayesian Logistic Regression (BLogR) method. Utilizing these features, LogitBoost and Random Forest (LogitBoost-RF) classifier gives the better classification accuracy among all the classifiers. In Chapter 6, Fast Radial Symmetry Transform (FRST) is applied to mammographic images for derivation of radially symmetric features. A t-distributed Stochastic Neighbor Embedding (t-SNE) method has been utilized to select most relevant features. Using these features, classification experiments have been carried out through all the classifiers. A Logistic Model Tree (LMT) classifier achieves optimal results among all classifiers. An overall comparative analysis has also been made among all our suggested features and feature reduction techniques along with the corresponding classifier where they show superior results