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PubMedDate not supplied

Semiempirical quantum chemical method and artificial neural networks applied for lambdamax computation of some azo dyes.

The maximum absorption wavelengths of 31 azo dyes have been calculated by two comprehensive methods using the semiempirical quantum chemical method, PM3, and the weight decay based artificial neural network (WD-ANN) or the early stopping based artificial neural network (ES-ANN). The average absolute errors of WD-ANN and that of ES-ANN are 10.07 nm and 12.40 nm, respectively. These results are much better than the results using ZINDO/S with the default value (0.585) only.

Journal Article↗

PubMed1996

Determination of dependent and independent communication paths using neural networks.

Efficient and timely computation of routing algorithms is very important for proper operation of multihop packet radio networks. When a network operates in the presence of jammers (a hostile environment) additional constraints must be considered when computing the routing tables. Insertion of those constraints in routing algorithms enables the determination of dependent and independent paths between source and destination nodes, thus fixing lower limits on the number of external jamming sources required to sever a transmission. An important measure of network reliability is also provided. This paper proposes a solution to the routing problem through the implementation of a Hopfield network and demonstrates that by proper selection of an energy function we can solve the problem rapidly and reliably.

Algorithms↗

PubMed1992

Computer recognition of brain stem auditory evoked potential wave V by a neural network.

A neural network simulator was used for the recognition of the presence and location of the peak of wave V of the brain stem auditory evoked potential (BAEP) test. Waveforms selected from BAEPs performed in the last 4 years at the University of Pittsburgh Presbyterian University Hospital were digitized and sampled, and the resulting amplitudes were normalized. A training set was composed of the waveforms resulting from the stimulation of 50 ears. The normalized amplitudes were used as the initial activation values for the networks. The desired outputs (the target locations for wave V) were represented in the output layer by setting the output element, which corresponded to the target location and its immediate neighbors, to high activation levels, and all the remaining output units to zero activity. Two network architectures, differing only in the hidden unit layer, with 40 and 16 hidden units, respectively, were trained by using standard back-propagation. Several trials from different starting points were performed for each architecture. The best network, found after 60 epochs (3,000 presentations), was able to correctly identify 17 of 20 cases (85%) from a set of test cases that were independent from the training set.

Evaluation Studies as Topic↗

PubMed2003

Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks.

An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. The method was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T(2) weighted) and from a clinical study of human Alzheimer's disease (T(1) weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi-automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88).

Abscess↗

PubMed1994

Cell suicide in the developing nervous system: a functional neural network model.

A computational model of programmed cell death (PCD) in the nervous system is described. A neurobiologically realisable method for identifying and removing the least useful cells from a network is developed, and it is shown by simulation that an artificial neural network can solve difficult problems efficiently if it is given more neurons initially than it needs subsequently. The least useful neurons die off gradually after learning is complete, and the learned solution can then be maintained with a smaller number of units than were needed for initial learning. The research suggests a functional role for PCD, and how self-limiting PCD could be achieved in real neural systems.

Apoptosis↗

PubMed1999

Neural network prediction of obstructive sleep apnea from clinical criteria.

STUDY OBJECTIVES: Clinical prediction models for the diagnosis of obstructive sleep apnea (OSA) have lacked the accuracy necessary to confidently replace polysomnography (PSG). Artificial neural networks are computer programs that can be trained to predict outcomes based on experience. This study was conducted to test the hypothesis that a generalized regression neural network (GRNN) could accurately classify patients with OSA from clinical data. STUDY DESIGN: Retrospective review. SETTING: Regional sleep referral center. PATIENTS: Randomly selected records of patients referred for possible OSA. MEASUREMENTS: The neural network was trained using 23 clinical variables from 255 patients, and the predictive performance was evaluated using 150 other patients. RESULTS: The prevalence of OSA in this series of 405 patients (293 men and 112 women) was 69%. The trained GRNN had an accuracy of 91.3% (95% confidence interval [CI], 86.8 to 95.8). The sensitivity was 98.9% for having OSA (95% CI, 96.7 to 100), and the specificity was 80% (95% CI, 70 to 90). The positive predictive value that the patient would have OSA was 88.1% (95% CI, 81.8 to 94.4), whereas the negative predictive value that the patient would not have OSA (if so classified) was 98% (95% CI, 94 to 100). CONCLUSIONS: Appropriately trained GRNN has the ability to accurately rule in OSA from clinical data, and GRNN did not misclassify patients with moderate to severe OSA. In this study, use of the neural network could have reduced the number of PSG studies performed. Prospective validation of the neural network for the diagnosis of OSA is now required.

Adolescent↗

PubMed1994

Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network.

A computer-aided diagnosis (CAD) scheme has been developed in our laboratory for the detection of clustered microcalcifications in digital mammograms. In this study, we apply a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme. The shift-invariant neural network is a multilayer back-propagation neural network with local, shift-invariant interconnections. The advantage of the shift-invariant neural network is that the result of the network is not dependent on the locations of the clustered microcalcifications in the input layer. The neural network is trained to detect each individual microcalcification in a given region of interest (ROI) reported by the CAD scheme. A ROI is classified as a positive ROI if the total number of microcalcifications detected in the ROI is greater than a certain number. The performance of the shift-invariant neural network was evaluated by means of a jackknife (or holdout) method and ROC analysis using a database of 168 ROIs, as reported by the CAD scheme when applied to 34 mammograms. The analysis yielded an average area under the ROC curve (Az) of 0.91. Approximately 55% of false-positive ROIs were eliminated without any loss of the true-positive ROIs. The result is considerably better than that obtained in our previous study using a conventional three-layer, feed-forward neural network. The effect of the network structure on the performance of the shift-invariant neural network is also studied.

Biophysical Phenomena↗

PubMedDate not supplied

Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental numerical models.

A new generic neural network (NN) application-improving computational efficiency of certain processes in numerical environmental models-is considered. This approach can be used to accelerate the calculations and improve the accuracy of the parameterizations of several types of physical processes which generally require computations involving complex mathematical expressions, including differential and integral equations, rules, restrictions and highly nonlinear empirical relations based on physical or statistical models. It is shown that, from a mathematical point of view, such parameterizations can usually be considered as continuous mappings (continuous dependencies between two vectors) and, therefore, NNs can be used to replace primary parameterization algorithms. In addition to fast and accurate approximation of the primary parameterization, NN also provides the entire Jacobian for very little computation cost. Four particular real-life applications of the NN approach are presented here: for oceanic numerical models, a NN approximation of the UNESCO equation of state of the sea water (NN for the density of the seawater) and an inversion of this equation (NN for the salinity of the seawater); for atmospheric numerical models, a NN approximation for long wave radiative transfer code; and for wave models, a NN approximation for the nonlinear wave-wave interaction. In all considered applications a significant acceleration of numerical computations has been achieved. The first two of these NN applications have already been implemented in the multi-scale ocean forecast system at NCEP. The NN approach introduced in this paper can provide numerically efficient solutions to a wide range of problems in numerical models where lengthy, complicated calculations, which describe physical, chemical and/or biological processes, must be repeated frequently.

Environment↗

PubMed1999

Discontinuities in recurrent neural networks.

This article studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net function and a sigmoid-like continuous activation function. We introduce arithmetic networks as ARNN augmented with a few simple discontinuous (e.g., threshold or zero test) neurons. We argue that even with weights restricted to polynomial time computable reals, arithmetic networks are able to compute arbitrarily complex recursive functions. We identify many types of neural networks that are at least as powerful as arithmetic nets, some of which are not in fact discontinuous, but they boost other arithmetic operations in the net function (e.g., neurons that can use divisions and polynomial net functions inside sigmoid-like continuous activation functions). These arithmetic networks are equivalent to the Blum-Shub-Smale model, when the latter is restricted to a bounded number of registers. With respect to implementation on digital computers, we show that arithmetic networks with rational weights can be simulated with exponential precision, but even with polynomial-time computable real weights, arithmetic networks are not subject to any fixed precision bounds. This is in contrast with the ARNN that are known to demand precision that is linear in the computation time. When nontrivial periodic functions (e.g., fractional part, sine, tangent) are added to arithmetic networks, the resulting networks are computationally equivalent to a massively parallel machine. Thus, these highly discontinuous networks can solve the presumably intractable class of PSPACE-complete problems in polynomial time.

Algorithms↗

PubMed2003

A model for emergent complex order in small neural networks.

A new neural network model is introduced in this paper. The aim of the proposed Sierpinski neural networks is to provide a simple and biologically plausible neural network architecture that produces emergent complex spatio-temporal patterns through the activity of the output neurons of the network and is able to perform computational tasks. Such networks may play an important role in the analysis and understanding of complex dynamic activity observed at various levels of biological neural systems. The proposed Sierpinski neural networks are described in detail and their functioning is analyzed. We discuss about emerging neural activity patterns and their interpretations, neuro-computation with such emerging activity patterns, and also possible implications for computational neuroscience.

Animals↗

PubMed1996

Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.

AIMS: To determine if neural networks can detect diabetic features in fundus images and compare the network against an ophthalmologist screening a set of fundus images. METHODS: 147 diabetic and 32 normal images were captured from a fundus camera, stored on computer, and analysed using a back propagation neural network. The network was trained to recognise features in the retinal image. The effects of digital filtering techniques and different network variables were assessed. 200 diabetic and 101 normal images were then randomised and used to evaluate the network's performance for the detection of diabetic retinopathy against an ophthalmologist. RESULTS: Detection rates for the recognition of vessels, exudates, and haemorrhages were 91.7%, 93.1%, and 73.8% respectively. When compared with the results of the ophthalmologist, the network achieved a sensitivity of 88.4% and a specificity of 83.5% for the detection of diabetic retinopathy. CONCLUSIONS: Detection of vessels, exudates, and haemorrhages was possible, with success rates dependent upon preprocessing and the number of images used in training. When compared with the ophthalmologist, the network achieved good accuracy for the detection of diabetic retinopathy. The system could be used as an aid to the screening of diabetic patients for retinopathy.

Diabetic Retinopathy↗

PubMed1999

Neural network in communication with medical computer system.

The paper presents a concept of an experimental module designed to recognise spoken utterances that cover a limited range of words indispensable in dialogues with computer medical systems. Research into the recognition of spoken words by a module based on artificial neural network is described. Usefulness of the obtained results for surgery-assisting multimedia systems and for a patient simulator supporting medical education of students in case history-taking and diagnosing is also discussed.

Artificial Intelligence↗

PubMed1994

An associative memory that can form hypotheses: a phase-coded neural network.

Nonlinear associative memories as realized, e.g., by Hopfield nets are characterized by attractor-type dynamics. When fed with a starting pattern, they converge to exactly one of the stored patterns which is supposed to be most similar. These systems cannot render hypotheses of classification, i.e., render several possible answers to a given classification problem. Inspired by von der Malsburg's correlation theory of brain function, we extend conventional neural network architectures by introducing additional dynamical variables. Assuming an oscillatory time structure of neural firing, i.e., the existence of neural clocks, we assign a so-called phase to each formal neuron. The phases explicitly describe detailed correlations of neural activities neglected in conventional neural network architectures. Implementing this extension into a simple self-organizing network based on a feature map, we present an associative memory that actually is capable of forming hypotheses of classification.

Animals↗

PubMed1992

Application of neural networks in medical diagnosis: the case of sexually-transmitted diseases.

In this paper neural networks are used to formulate a means of diagnosing sexually-transmitted diseases (STD's). An overview of the theoretical background of neural networks is presented. A summary of the diseases used in the project and their signs and symptoms are given. These signs and symptoms are used to train a network. Upon presentation of a set of signs and symptoms to the trained network, the presence of a disease or diseases with those symptoms may be deduced.

Diagnosis, Computer-Assisted↗

PubMed1999

Approximation techniques for neuromimetic calculus.

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary least squares (LS) are emphasized.

Algorithms↗

PubMed2006

Why does brain damage impair memory? A connectionist model of object recognition memory in perirhinal cortex.

Object recognition is the canonical test of declarative memory, the type of memory putatively impaired after damage to the temporal lobes. Studies of object recognition memory have helped elucidate the anatomical structures involved in declarative memory, indicating a critical role for perirhinal cortex. We offer a mechanistic account of the effects of perirhinal cortex damage on object recognition memory, based on the assumption that perirhinal cortex stores representations of the conjunctions of visual features possessed by complex objects. Such representations are proposed to play an important role in memory when it is difficult to solve a task using representations of only individual visual features of stimuli, thought to be stored in regions of the ventral visual stream caudal to perirhinal cortex. The account is instantiated in a connectionist model, in which development of object representations with visual experience provides a mechanism for judgment of previous occurrence. We present simulations addressing the following empirical findings: (1) that impairments after damage to perirhinal cortex (modeled by removing the "perirhinal cortex" layer of the network) are exacerbated by lengthening the delay between presentation of to-be-remembered items and test, (2) that such impairments are also exacerbated by lengthening the list of to-be-remembered items, and (3) that impairments are revealed only when stimuli are trial unique rather than repeatedly presented. This study shows that it may be possible to account for object recognition impairments after damage to perirhinal cortex within a hierarchical, representational framework, in which complex conjunctive representations in perirhinal cortex play a critical role.

Brain Injuries↗

PubMed1997

Current keratoconus detection methods compared with a neural network approach.

PURPOSE: Four videokeratographic methods for keratoconus detection were compared with a neural network approach. METHODS: A classification neural network for keratoconus screening was designed to detect the presence of keratoconus (KC) or keratoconus suspects (KCS); a separate cone severity network graded the severity of conelike topography patterns consistent with KC or KCS. Three hundred TMS-1 examinations (Tomey) were randomly divided into training and test sets. Ten topographic indexes were network inputs. Nine categories were used: normal, astigmatism, KC, KCS, contact lens-induced warpage, pellucid marginal degeneration, photorefractive keratectomy, radial keratotomy, and penetrating keratoplasty. KC was subdivided into KC1 (mild), KC2 (moderate), and KC3 (advanced). There were three outputs for the classification network (KC, KCS, and OTHER); target output values of 0 = OTHER, 0.25 = KCS, 0.5 = KC1, 0.75 = KC2, and 1.0 = KC3 were used for the severity network. RESULTS: The best-trained classification network had 100% accuracy, specificity, and sensitivity for the test set. The severity network had mean outputs (+/-standard deviation) of OTHER = 0.02+/-0.02, KCS = 0.21+/-0.05, KC1 = 0.52+/-0.17, KC2 = 0.74+/-0.12, and KC3 = 0.91+/-0.15. The severity network output for all categories was well correlated to the keratoconus prediction index (R = 0.892, P < 0.0001). The classification network had an overall accuracy and specificity significantly better (P < or = 0.005) than the Klyce/Maeda keratoconus index (KCI) test, the Rabinowitz test (K & I-S), and simulated keratometry (average Sim K). However, there were no significant differences in keratoconus sensitivity between the classification network, KCI, and K & I-S. The sensitivity and specificity of average Sim K were significantly worse than those of the other tests. The classification network had significantly better sensitivity (P < 0.001) and specificity (P = 0.025) for KCS detection than the K & I-S. CONCLUSIONS: The neural networks completely distinguished KC from KCS and from topographies that resembled KC. The network approach equaled the sensitivity of currently used tests for keratoconus detection and outperformed them in terms of accuracy and specificity.

Cornea↗

PubMed1995

Identification of a hypoperfused segment in bull's-eye myocardial perfusion images using a feed forward neural network.

Artificial neural networks are computer systems which can be trained to recognize similarities in patterns and which learn by example; one of the more straightforward types being the feed forward neural network (FFNN). We previously reported the use of FFNNs for classification of hypoperfusion patterns in bull's-eye representation of 201Tl single photon emission tomography myocardial perfusion studies and showed that, when such an image was divided into 24 segments, FFNNs could detect perfusion defects without direct comparison to a normal data base. This has been extended in this investigation to assess the ability of an FFNN, trained on data in which only a single segment was hypoperfused, to detect this abnormal segment when the hypoperfusion pattern of the other segments in the image varied. The results indicated that the network could reliably determine whether a segment was normally or under perfused, with accuracies of 99% and 100%, respectively, if all other segments were normally perfused. It could also reliably detect a normally perfused segment, even if other segments were hypoperfused, with accuracies of 95% and 98%. The network was less reliable, however, in detecting a hypoperfused segment when other segments were also hypoperfused, showing accuracies of only 74% and 88%.

Blood Flow Velocity↗