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Download Free PDFA Comparison of State-of-the-Art Classification Techniques with Application to CytogeneticsBoaz Lerner

2001, Neural Computing & Applications

https://doi.org/10.1007/S005210170016visibility

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Abstract

Page 1. Neural Comput & Applic (2001)10:39–47 © 2001 Springer-Verlag London Limited A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics Boaz Lerner and Neil D. Lawrence Computer ...

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A hybrid intelligent diagnostic system based on neural networks and image analysis techniques in the field of automated cytogeneticsaydan erkmen, Funda Basaran, S. Eskiizmirliler

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We introduce a hybrid intelligent karyotyping system based on two different types of artificial neural networks (ANNs) and chromosome's features obtained by digital image processing techniques. A microscope equipped with a CCTV camera and a microcomputer including a frame grabber are the basic components of our hardware set-up. The inputs to the ANN structure are obtained directly from digital chromosome images by using two recently developed object detection and object skeletonizing algorithms. Moreover, the band patterns of chromosomes are represented by applying wavelet transform techniques on the gray level profiles of chromosomes. The network parameters are determined by using the results of many training and testing experiments in order to reach an optimal state from the classification performance point of view

downloadDownload free PDFView PDFchevron_rightApplication of Ensemble Machines of Neural Networks to Chromosome ClassificationMehmet Can

Southeast Europe Journal of Soft Computing, 2012

This work presents approaches to the automatic classification of metaphase chromosomes using several perceptron neural network techniques on neural networks function as committee machines. To represent the banding patterns, only chromosome gray level profiles are exploited. The other inputs to the ensemble machines of the network are the chromosome size and centromeric index. It is shown that, without much effort, the classification performances of the four networks are found to be similar to the ones of a well-developed parametric classifier. Four parallel networks trained for the four different aspects of the data set, the gray level profile vector, Fourier coefficients of gray level profiles, 3D data of chromosome length -centromeric index -total gray levels, and 4D data obtained by the addition of average gray levels. Then the classification results of differently trained neural networks (i.e., experts), are combined by the use of a genuine ensemble-averaging to produce an overall output by the combiner. We discuss the flexibility of the classifier developed, its potential for development, and how it may be improved to suit the current needs in karyotyping.

downloadDownload free PDFView PDFchevron_rightNew features for automatic classification of human chromosomes: A feasibility studykamal setarehdan

Pattern Recognition Letters, 2006

Karyotyping, a standard method for presenting pictures of the human chromosomes for diagnostic purposes, is a long standing, yet common technique in cytogenetics. Automating the chromosome classification process is the first step in designing an automatic karyotyping system. The main aim in this study was to define a new group of features for better representation and classification of chromosomes. Width, position and the average intensity of the two most eye-catching regions of each chromosome (that we call characteristic bands) are the new proposed features. The concept of a characteristic band is based on the expert cytogeneticistsÕ method in classification of the chromosomes. The length, centromeric index (CI) and an index of overall darkness or brightness of the image (NAGD) were also included in the final nine-dimensional feature vectors describing each chromosome. To automatically find the characteristic bands and calculate the new features, different windows in chromosomeÕs density profile were scored based on their intensity and width. As a feasibility study, our work was focused on classification of chromosomes in group E. Three layer artificial neural networks were employed to classify each chromosome in one of the three possible classes (chromosomes 16, 17 and 18). The best results obtained were accurate classification of up to 98.6% of chromosomes. Particularly a six-dimensional subset of the features showed reproducibly high performances in classification experiments. The results of this feasibility study show that new features inspired from human expertÕs classification method are potentially capable of improving the accuracy of the karyotyping systems.

downloadDownload free PDFView PDFchevron_rightChromosome identification using hidden Markov models: comparison with neural networks, singular value decomposition, principal components analysis, and Fisher discriminant analysisTimothy O'Leary

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downloadDownload free PDFView PDFchevron_rightSuperiority of artificial neural networks for a genetic classification procedureL. Bhering

Genetics and Molecular Research, 2015

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing Artificial neural networks in genetic classification ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (3): 9898-9906 (2015) their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

downloadDownload free PDFView PDFchevron_rightComputer-aided classification of human chromosomes: a reviewAndrew Carothers

Statistics and Computing, 1994

Computer-aided imaging systems are now widely used in cytogenetic laboratories to reduce the tedium and labour-intensiveness of traditional methods of chromosome analysis. Automatic chromosome classification is an essential component of such systems, and we review here the statistical techniques that have contributed towards it. Although completely error-free classification has not been, nor is ever likely to be, achieved, error rates have been reduced to levels that are acceptable for many routine purposes. Further reductions are likely to be achieved through advances in basic biology rather than in statistical methodology. Nevertheless, the subject remains of interest to those involved in statistical classification, because of its intrinsic challenges and because of the large body of existing results with which to compare new approaches. Also, the existence of very large databases of correctly-classified chromosomes provides a valuable resource for empirical investigations of the statistical properties of classifiers.

downloadDownload free PDFView PDFchevron_rightAn empirical study of fuzzy ARTMAP applied to cytogeneticsBoaz Lerner

2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, 2004

The fuzzy ARTMAP (FAM) neural network is evaluated in a pattern classification task of discriminating signals identifying genetic diseases. The FAM provides incremental learning necessary to cope with the expansion of genetic applications and variety of biological preparation techniques. Two training modes of the FAM, training until completion and training with validation, are experimentally compared with respect to their accuracy and sensitivity to the vigilance parameter. Although overfitting the training set, the FAM accuracy on the test set after being trained until completion outperforms that achieved utilizing a validation set. This classification accuracy is completed employing less than five epochs compared to hundreds of training epochs required for other neural network paradigms to accomplish similar performance.

downloadDownload free PDFView PDFchevron_rightToward a completely automatic neural-network-based human chromosome analysisBoaz Lerner

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1998

The application of neural networks (NN's) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chromosome analyzers usually fail to accomplish, is performed. First, a moment representation of the image pixels is clustered to create a binary image without a need for threshold selection. Based on the binary image, lines connecting cut points imply possible separations. These hypotheses are verified by a multilayer perceptron (MLP) NN that classifies the two segments created by each separating line. Use of a classification-driven segmentation process gives very promising results without a need for shape modeling or an excessive use of heuristics. In addition, an NN implementation of Sammon's mapping using principal component based initialization is applied to feature extraction, significantly reducing the dimensionality of the feature space and allowing high classification capability. Finally, by applying MLP based hierarchical classification strategies to a well-explored chromosome database, we achieve a classification performance of 83.6%. This is higher than ever published on this database and an improvement of more than 10% in the error rate. Therefore, basing a chromosome analysis on the NN-based techniques that are developed in this research leads toward a completely automatic human chromosome analysis.

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The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

A large number of different neural network structures have been constructed, trained and tested to a large data base of pregnant women characteristics, aiming at generating a classifier-predictor for the presence of chromosomal abnormalities in fetuses, namely the Trisomy 21 (Down syndrome), Trisomy 18 (Edwards syndrome), Trisomy 13 (Patau syndrome) and the Turner syndrome. The database was composed of 31611 cases of pregnant women. 31135 women did not show any chromosomal abnormalities, while the remaining 476 were confirmed as having a chromosomal anomaly of T21, T18, T13, or Turner Syndrome. From the total of 31611 cases, 8191 were kept as a totally unknown database that was only used for the verification of the predictability of the network. In this set, 7 were of the Turner syndrome, 14 of the Patau syndrome, 42 of the Edwards syndrome and 71 of the Down syndrome. For each subject, 10 parameters were considered to be the most influential at characterizing the risk of occurrence of these types of chromosomal anomalies. The best results were obtained when using a multi-layer neural structure having an input, an output and three hidden layers. For the case of the totally unknown verification set of the 8191 cases, 98.1% were correctly identified. The percentage of abnormal cases correctly predicted was 85.1%. The unknown T21 cases were predicted by 78.9%, the T18 by 76.2%, the T13 by 0.0% and the Turner syndrome by 42.9%.

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Infertility is an issue that affects roughly 186 million people worldwide. This issue is partially overcome using fertility treatments that often produce in vitro embryos, which undergo a visual morphology assessment to determine embryo quality. The potential of using neural networks to aid this subjective process has barely been examined. In this thesis, we researched the potential of using STORK, Inception V1 and Inception V3 as tools to aid in the qualitative classification of embryos produced in vitro at the MCK Fertility Center. We did this by analyzing how accurately these neural networks could predict whether embryos had been selected for transfer into the uterine cavity. The analysis shows that Inception V1 and Inception V3 had an accuracy of 68% and 78% respectively, while STORK lagged behind at roughly 50%. In conclusion, neural networks are a potentially viable addition to the embryo quality assessment process, although further research is required.

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