A contour code feature based segmentation for handwriting recognition

The purpose of this paper is to present a novel contour code feature in conjunction with a rule based segmentation for cursive handwriting recognition. A heuristic segmentation algorithm is initially used to over segment each word. Then the prospective segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. The proposed rule-based module validates every segmentation points against closed area, average character size, left character and density. During the left char validation, a contour code feature is extracted and checked weather the left of the prospective segmentation point is a character or rubbish (non-char). The neural network used for this validation was trained on character and non-character database. Following the segmentation, the contour between correct segmentation points is passed through the feature extraction module that extracts the contour code, after which another trained neural network is used for classification. The recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that has highest confidence value

A Neural learning algorithm for the diagnosis of breast cancer

This paper presents a new learning algorithm for the diagnosis of breast cancer. The proposed algorithm with novel network architecture can memorize training patterns with 100% retrieval accuracy as well as achieve high generalization accuracy for patterns which it has never seen before. The grey-level and BI-RADS features (radiologists’ interpretation) from digital mammograms are extracted and used to train the network with the proposed learning algorithm. The new learning algorithm has been implemented and tested on a DDSM Benchmark database. The proposed approach has out performed other existing approaches interms of classification rate, generalization and memorization abilities, number of iterations, fast and guaranteed training. Some promising results and a comparative analysis of obtained results are included in this paper

Impact of multiple clusters on neural classification of ROIs in digital mammograms

This paper evaluates the impact of multiple clusters on neural classification of regions of interest (ROIs) in digital mammograms. The training and test sets for neural networks usually contain inputs extracted from ROIs and relevant class such as benign and malignant. However, the patterns such as regions of interest in digital mammograms do not have just one cluster per class instead they have many clusters within benign and malignant classes. Therefore, neural network training may benefit in terms of accuracy and efficiency by creating and analyzing a number of clusters within a class. A novel multiple clusters based neural classification approach is presented. In this approach, input data is clustered into a number of clusters per class and a neural classifier is trained with clustered data which contain multiple clusters per class. The experiments on a benchmark database of digital mammograms are conducted. The results show that the multiple clusters per class have significant impact on neural classification and overall they achieve better accuracy than single cluster per class based classification of ROIs in digital mammograms

Neural network based classifier ensembles : a comparative analysis

This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare our recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. First type is a classifier ensemble that uses a neural network for all its base classifiers. Second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. Third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository

Multicluster class-based classification for the diagnosis of suspicious areas in digital mammograms

This book chapter presents a multi-cluster class based classification approach for the classification of suspicious areas extracted from digital mammograms into benign and malignant classes. The approach creates multiple clusters and selects strong clusters for each class. The created strong clusters are used to form subclasses within benign and malignant classes and training of a classifier. The creation of strong multiple clusters during the classification process can improve the accuracy of the classification system. The experiments using multi-cluster class based approach and a standard classifier with a single cluster per class have been conducted on a benchmark database of digital mammograms. The results have shown that the multi-cluster class based approach makes a significant impact on improving the classification accuracy

Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms

The main objective of this paper is to present a novel learning algorithm for the classification of mass abnormalities in digitized mammograms. The proposed approach consists of new network architecture and a new learning algorithm. The original idea is based on the introduction of an additional neuron in the hidden layer for each output class. The additional neurons for benign and malignant classes help in improving memorization ability without destroying the generalization ability of the network. The training is conducted by combining minimal distance based similarity/random weights and direct calculation of output weights. The proposed approach can memorize training patterns with 100% retrieval accuracy as well as achieve high generalization accuracy for patterns which it has never seen before. The grey-level and breast imaging reporting and data system based features from digitized mammograms are extracted and used to train the network with the proposed architecture and learning algorithm. The best results achieved by using the proposed approach are 100% on training set and 94% on test set

Impact of multiple clusters on neural classification of ROIs in digital mammograms

This paper evaluates the impact of multiple clusters on neural classification of regions of interest (ROIs) in digital mammograms. The training and test sets for neural networks usually contain inputs extracted from ROIs and relevant class such as benign and malignant. However, the patterns such as regions of interest in digital mammograms do not have just one cluster per class instead they have many clusters within benign and malignant classes. Therefore, neural network training may benefit in terms of accuracy and efficiency by creating and analyzing a number of clusters within a class. A novel multiple clusters based neural classification approach is presented. In this approach, input data is clustered into a number of clusters per class and a neural classifier is trained with clustered data which contain multiple clusters per class. The experiments on a benchmark database of digital mammograms are conducted. The results show that the multiple clusters per class have significant impact on neural classification and overall they achieve better accuracy than single cluster per class based classification of ROIs in digital mammograms

Combination strategies for finding optimal neural network architecture and weights

The chapter presents a novel neural learning methodology by using different combination strategies for finding architecture and weights. The methodology combines evolutionary algorithms with direct/matrix solution methods such as Gram-Schmidt, singular value decomposition, etc., to achieve optimal weights for hidden and output layers. The proposed method uses evolutionary algorithms in the first layer and the least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons and weights using hierarchical combination strategies. The chapter explores all the different facets of the proposed method in tenns of classification accuracy, convergence property, generalization ability, time and memory complexity. The learning methodology has been tested using many benchmark databases such as XOR, 10 bit odd parity, handwriting characters from CEDAR, breast cancer and heart disease from DCI machine learning repository. The experimental results, detailed discussion and analysis are included in the chapter

A non-iterative radial basis function based quick convolutional neural network

In the past few years, Convolutional Neural Networks (CNNs) have achieved surprisingly good results for objects classification in real world images. However, training a CNN from scratch for large datasets is still a nightmare, when it comes to time and resources. The main reason for this problem is long iterative training process used in CNN's fully connected layer which is also called a classification layer. Therefore, in this paper we propose a novel approach to make the convolutional neural network quicker and more efficient for image classification tasks. The proposed approach consists of a convolutional feature extraction layer and a non-iterative radial basis function-based classification layer. The proposed approach has been evaluated on three benchmark datasets such as CIFAR-10, MNIST and Digit. The experimental results have demonstrated that the proposed approach can achieve same or higher accuracy in lesser time than the standard CNN

Parallel neural-based hybrid data mining ensemble

This paper presents a novel hybrid data mining ensemble approach which is an effective combination of various clustering methods, in order to utilize the strengths of individual technique and compensate for each other’s weaknesses. The proposed approach is formulated to cluster extracted features into ‘soft’ clusters using unsupervised learning strategies and fuse the cluster decisions using parallel fusion in conjunction with a neural classifier. The proposed approach has been implemented and evaluated on the benchmark databases such as Digita lDatabase for Screening Mammograms, Wisconsin Breast Cancer and ECG Arrhythmia. A comparative performance analysis of the proposed hybrid data mining approach with other existing approaches is presented. The experimental results demonstrate the effectiveness of the proposed approach