Neural network based ensemble classifier for digital mammography

Breast cancer is a debilitating condition that has a high mortality and morbidity rate and is the most commonly diagnosed form of cancer in women. The aetiology of the disease is not known however mitigating lifestyle and genetic conditions are known to increase the likelihood of contracting the condition. This thesis proposes a number of techniques designed to increase the diagnostic accuracy of Computer Aided Diagnostic (CADx) systems. The first technique involved breaking down the benign and malignant classes into sub-classes through clustering and using a support vector machine classifier on these sub-classes in a technique known as Soft Clustered Support Vector Machine (SCSVM). The sub-classes are designed to reduce the variability within the classes in order to increase classification accuracy. The second technique attempted to use clustering in two complimentary mechanisms. The first was to retrieve those sub-classes generated through clustering that readily identified to a cluster by retrieving the cluster label. The second mechanism uses the variability in cluster membership of k-means clustering to introduce diversity into a feed forward neural network for the construction of an ensemble. The idea is that clustering processes the easily classified patterns allowing the ensemble to be trained on the harder-to-classify patterns. This technique is known as a Clustered Ensemble Neural Network (CENN). The third technique utilizes changing the number of neurons of a single hidden layer of feed forward neural networks. These candidate networks are then ranked in terms of performance in order to create an ensemble classifier which is combined or fused together using the majority vote algorithm. The technique is known as a Variable Hidden Neuron Ensemble Classifier (VHNEC). Only three base classifiers are needed to obtain high classification accuracy. During the creation of the VHNEC it is found that it is possible to predict a range that holds the best performing networks utilizing the number of hidden neurons through polynomial regression. The SCSVM produces a classification accuracy of 94%, which is 4% higher than that achieved by a Support Vector Machine classifier on a dataset from the Digital Database of Screening Mammography (DDSM). The CENN technique achieves a classification accuracy of 91% on mass anomalies from the DDSM and while the improvement in accuracy is not high the CENN classifier reduces the variability in classification accuracy. The VHNEC technique is tested on mass anomalies from the DDSM and biopsy samples from the Wisconsin Breast Cancer dataset and achieves 99% classification accuracy. The research also identifies a mechanism (polynomial regression) to determine the upper and lower boundaries for predicting the number of neurons in the hidden layer that results in the best classification accuracy for a neural network being identified. The technique successfully predicts the range that contains the highest performing classifiers on the DDSM and Wisconsin Breast Cancer datasets. Considering that breast cancer can be a fatal condition1-3 and that the research presented achieves a high degree of accuracy on breast masses and can reduce the resources (time, cpu and memory) in configuring and operating computer aided diagnostic systems it is highly significant. This thesis also makes recommendations about possible improvements to the proposed techniques including suggestions for future research

Effects of large constituent size in variable neural ensemble classifier for breast mass classification

This paper proposes a novel ensemble technique for mass classification in digital mammograms by varying the number of hidden units to create diverse candidates. The effects of adding more networks to the ensemble are evaluated on a mammographic database and the results are presented. A classification accuracy of ninety nine percent is achieved

Multi-cluster support vector machine classifier for the classification of suspicious areas in digital mammograms

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on a novel idea of clustering input data into numerous (soft) clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called Multi-Cluster Support Vector Machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed SCSVM technique has been evaluated on data from the DDSM benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant

Clustering and least square based neural technique for learning and identification of suspicious areas within digital mammograms

This paper presents a technique which explores the fusion of clustering and a least square method for the classification of suspicious areas within digital mammograms into benign and malignant classes. It incorporates clustering algorithm such as k-means in conjunction with a gram-schmidt based least square method. The main focus of the research presented in this paper is to (1) improve the classification of features from suspicious areas within digital mammograms and (2) examine the effects that the determined clusters and least square methods have on classification accuracy and efficiency. The proposed technique has been tested on a benchmark database and the results from preliminary experiments are discussed

A multilayered ensemble architecture for the classification of masses in digital mammograms

This paper proposes a technique for the creation of a neural ensemble that introduces diversity through incorporating ten-fold cross validation together with varying the number of neurons in the hidden layer during network training. This technique is utilized to improve the classification accuracy of masses in digital mammograms. The proposed technique has been tested on a widely available benchmark database

A classifier with clustered sub classes for the classification of suspicious areas in digital mammograms

This paper presents a novel methodology for the classification of suspicious areas in digital mammograms. The methodology is based on the fusion of clustered sub classes with various intelligent classifiers. A number of classifiers have been incorporated into the proposed methodology and evaluated on the well known benchmark digital database of screening mammography (DDSM). The results in the form of overall classification accuracies, TP, TN, FP and FN have been analyzed, compared and presented. The results of all four tested classifiers with clustered sub classes on the DDSM benchmark database show that the proposed methodology can significantly improve the accuracy and reduce the false positive rate

Clustered ensemble neural network for breast mass classification in digital mammography

This paper proposes the creation of an ensemble neural network by incorporating a k-means classifier. This technique is designed to improve the classification accuracy of a multi-layer perceptron style network for mass classification of digital mammograms. The proposed technique has been tested on a benchmark database and the results have been contrasted with current research. The experimental results demonstrate that the accuracy of the proposed technique is comparable with existing systems

Polynomial prediction of neurons in neural network classifier for breast cancer diagnosis

Post hoc evaluation mechanisms are utilized for determining the configuration of classifiers. Heuristic approaches mean that sub-optimal configurations could be used; resulting in lost training time, sub-optimal performance and can result in inappropriate results especially for large complex datasets. This paper proposes a new technique to determine the number of neurons in feed forward neural network on two large-scale breast cancer datasets. Classification accuracy of 86% and 89.17% was achieved and the technique predicted the upper and lower bounds for neurons in the feed forward neural networks

Variable hidden neuron ensemble for mass classification in digital mammograms

This paper proposes a new ensemble technique for the classification of masses in digital mammograms based on neural networks with variable hidden neurons which are combined with hierarchical fusion. The main focus is introducing diversity into an ensemble network by varying the number of neurons in the hidden layer of the neural networks and ten-fold cross validation. The novelty of the proposed ensemble lies in the creation of diverse neural networks and combining the best performers using hierarchical fusion

Impact of soft clustering on classification of suspicious areas in digital mammograms

This paper investigates a soft cluster based approach for determining the impact of soft clustering on the training of a neural network classifier for the classification of suspicious areas in digital mammograms. An approach is proposed that first creates soft clusters for each available class and then uses soft clusters to form subclasses within benign and malignant classes. The incorporation of soft clusters in the classification process is designed to increase the learning abilities and improve the accuracy of the classification system. The experiments using soft clusters based proposed approach and a standard neural network classifier have been conducted on a benchmark database. The results have been analysed and presented in this paper