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Results for “Multilayer Perceptrons”

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PubMed2006

Excessive noise injection training of neural networks for markerless tracking in obscured and segmented environments.

In this letter, we demonstrate that the generalization properties of a neural network (NN) can be extended to encompass objects that obscure or segment the original image in its foreground or background. We achieve this by piloting an extension of the noise injection training technique, which we term excessive noise injection (ENI), on a simple feedforward multilayer perceptron (MLP) network with vanilla backward error propagation to achieve this aim. Six tests are reported that show the ability of an NN to distinguish six similar states of motion of a simplified human figure that has become obscured by moving vertical and horizontal bars and random blocks for different levels of obscuration. Four more extensive tests are then reported to determine the bounds of the technique. The results from the ENI network were compared to results from the same NN trained on clean states only. The results pilot strong evidence that it is possible to track a human subject behind objects using this technique, and thus this technique lends itself to a real-time markerless tracking system from a single video stream.

Electricity↗

PubMedDate not supplied

Neural networks in clinical medicine.

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications.

Algorithms↗

PubMed2005

A new approach to improve TMJ morphological information from plain film radiographs.

The hard tissues of the temporomandibular joint (TMJ) can be assessed through radiographic imaging to provide information to assist in diagnosis and treatment. However, the quality of information gathered from such imaging is often less than desired due to the small size of the TMJ, the widely varying fossa and condylar morphology, and the surrounding dense osseous structures. These make a clear and undistorted radiographic image of the hard tissue of the joint technically difficult. It is postulated that, if the degree of inaccuracy of a given radiograph is known quantitatively and taken into account, the clinician will be able to make a better informed qualitative assessment of TMJ morphology. The aim of this study is: 1. to improve the qualitative information that can be acquired from routine clinical plain film radiographs of the TMJ; 2. to use quantitative data to test the novel Neural Network (NN) model; and 3. to statistically show the discrepancies between routine radiographic images and the actual joint. Linear measurements of excised TMJs and their radiographic images were used to train a multilayer perceptron (MP) type NN model to predict joint dimensions more accurately. Such a neural network, developed using a statistical software package such as SPSS (SPSS, Inc. Chicago, IL), functions as a computer software program and predicts joint dimensions with increased accuracy when radiographic measurements are entered into the program. The NN model described here predicts the actual linear distances of the TMJ more closely than radiographic measurements and is more reliable in assessing the TMJ morphology.

Cadaver↗

PubMed2003

Intelligent data analysis to interpret major risk factors for diabetic patients with and without ischemic stroke in a small population.

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention.

Brain Infarction↗

PubMed2006

Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment.

Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO2) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment.

Algorithms↗

PubMed2025

Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.

BACKGROUND: The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale population cohort study. This study also aimed to assess ML model performance compared with traditional osteoporosis screening tools. Furthermore, this study aimed to examine the factors influencing the risk of osteoporosis through variable importance. METHODS: Data was collected from 4199 women aged 40-69 years in the baseline survey of the Ansan and Ansung cohort of the Korean Genome and Epidemiology Study. Osteoporosis was set as the dependent variable to develop ML classification models. Independent variables included 122 factors related to osteoporosis risk, such as socio-demographic characteristics, anthropometric parameters, lifestyle factors, reproductive factors, nutrient intakes, diet quality indices, medical history, medication history, family history, biochemical parameters, and genetic factors. The six classification models were developed using ML techniques, including decision tree, random forest, multilayer perceptron, support vector machine, light gradient boosting machine, and extreme gradient boosting (XGBoost). The six ML classification models were compared with two traditional osteoporosis screening tools, including the osteoporosis risk assessment instrument (ORAI) and the osteoporosis self-assessment tool (OST). The ML model performances were evaluated and compared using the confusion matrix and area under the curve (AUC) metrics. Variable importance was assessed using the XGBoost technique to investigate osteoporosis risk factors. RESULTS: The XGBoost model showed the highest performance out of the six ML classification models, with an accuracy of 0.705, precision of 0.664, recall of 0.830, and F1 score of 0.738. Moreover, the XGBoost model showed a higher performance on AUC than ORAI and OST. Variable importance scores were identified for 69 out of the 122 variables associated with osteoporosis risk factors. Age at menopause ranked first in variable importance. Variables of arthritis, physical activities, hypertension, education level, income level; alcohol intake, potassium intake, homeostatic model assessment for insulin resistance; energy intake, vitamin C intake, gout; and dietary inflammatory index ranked in the top 20 out of the 69 variables, using the XGBoost technique. CONCLUSIONS: This study found that an XGBoost model can be utilized to classify osteoporosis in Korean women. Age at menopause is a significant factor in osteoporosis risk, followed by arthritis, physical activities, hypertension, and education level.

Humans↗

PubMed2005

Papain entrapment in alginate beads for stability improvement and site-specific delivery: physicochemical characterization and factorial optimization using neural network modeling.

This work examines the influence of various process parameters (like sodium alginate concentration, calcium chloride concentration, and hardening time) on papain entrapped in ionotropically cross-linked alginate beads for stability improvement and site-specific delivery to the small intestine using neural network modeling. A 3(3) full-factorial design and feed-forward neural network with multilayer perceptron was used to investigate the effect of process variables on percentage of entrapment, time required for 50% and 90% of the enzyme release, particle size, and angle of repose. Topographical characterization was conducted by scanning electron microscopy, and entrapment was confirmed by Fourier transform infrared spectroscopy and differential scanning calorimetry. Times required for 50% (T(50)) and 90% (T(90)) of enzyme release were increased in all 3 of the process variables. Percentage entrapment and particle size were found to be directly proportional to sodium alginate concentration and inversely proportional to calcium chloride concentration and hardening time, whereas angle of repose and degree of cross-linking showed exactly opposite proportionality. Beads with >90% entrapment and T(50) of <10 minutes could be obtained at the low levels of all 3 of the process variables. The inability of beads to dissolve in acidic environment, with complete dissolution in buffer of pH >or=6.8, showed the suitability of beads to release papain into the small intestine. The shelf-life of the capsules prepared using the papain-loaded alginate beads was found to be 3.60 years compared with 1.01 years of the marketed formulation. It can be inferred from the above results that the proposed methodology can be used to prepare papain-loaded alginate beads for stability improvement and site-specific delivery.

Alginates↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed1992

Sleep classification in infants based on artificial neural networks.

The study reports on the possibility of classifying sleep stages in infants using an artificial neural network. The polygraphic data from 4 babies aged 6 weeks, 6 months and 1 year recorded over 8 hours were available for classification. From each baby 22 signals were recorded, digitized and stored on an optical disc. Subsets of these signals and additional calculated parameters were used to obtain data vectors, each of which represents an interval of 30 sec. For classification, two types of neural networks were used, a Multilayer Perceptron and a Learning Vector Quantizer. The teaching input for both networks was provided by a human expert. For the 6 sleep classes in babies aged 6 months, a 65% to 80% rate of correct classification (4 babies) was obtained for the testing data not previously seen.

Cerebral Cortex↗

PubMedDate not supplied

Estimating UV erythemal irradiance by means of neural networks.

In recent years, there has been a substantial increase in attempts to model the flux of ultraviolet radiation (UV). UV irradiance at surface level is a result of the combined effects of solar zenith angle, surface elevation, cloud cover, aerosol load and optical properties, surface albedo and the vertical profile of ozone. In this study, we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV irradiance on the basis of optical air mass, ozone columnar content, latitude, horizontal visibility data and cloud information such as type, coverage and height. ANN are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and, once trained, can perform prediction and generalization at high speed. In this study, a multilayer perceptron network (MLP) consisting of an input layer, an output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regulation back propagation algorithm. The study was developed using data from three stations on the Iberian Peninsula: Madrid and Murcia during the period 2000-2001 and Zaragoza in 2001. To train and validate the MPL neural networks, independent subsets of data were extracted from the complete database at each station. The results suggest that a MLP neural network using optical air mass, ozone columnar content, latitude and total cloud coverage provides the best estimates, with mean bias deviation and root mean square deviation of -0.1% and 18.0%, 1.6% and 19.6%, 0.1% and 14.6% at Madrid, Murcia and Zaragoza, respectively. Despite the dependence of the cloud radiative effect on cloud type, the use of additional information such as cloud type or cloud elevation did not improve these results. The performance of the developed ANN has been checked regarding its ability to estimate the UV index (UVI); results indicate that in more than 95% of the cases, the difference between estimated and measured values does not exceed one unit of UVI.

Journal Article↗

PubMed2005

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images.

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R2 = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Adult↗

PubMed2006

Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method.

The aim of this work was to predict the worldwide distribution of two pest species-Ceratitis capitata (Wiedemann), the Mediterranean fruit fly, and Lymantria dispar (L.), the gypsy moth-based on climatic factors. The distribution patterns of insect pests have most often been investigated using classical statistical models or ecoclimatic assessment models such as CLIMEX. In this study, we used an artificial neural network, the multilayer perceptron, trained using the backpropagation algorithm, to model the distribution of each species. The data matrix used to model the distribution of each species was divided into three data sets to (1) develop and train the model, (2) validate the model and prevent over-fitting, and (3) test each model on novel data. The percentage of correct predictions of the global distribution of each species was high for Mediterranean fruit fly for the three data sets giving 95.8, 81.5, and 80.6% correct predictions, respectively, and 96.8, 84.3, and 81.5 for the gypsy moth. Kappa statistics used to test the level of significance of the results were highly significant (in all cases P < 0.0001). A sensitivity analysis applied to each model based on the calculation of the derivatives of each of a large number of input variables showed that the variables that contributed most to explaining the distribution of C. capitata were annual average temperature and annual potential evapotranspiration. For L. dispar, the average minimum temperature and minimum daylength range were the main explanatory variables. The ANN models and methods developed in this study offer powerful additional predictive approaches in invasive species research.

Animals↗

PubMed1996

Experimental model and neural network based electrical conductivity estimation in soilless culture system.

The optimum management of nutrient solution in soilless culture needs the accurate control of nutrient solution, especially in recycled soilless culture system. To keep the electrical conductivity (EC) of nutrient solution within the adequate range after application of combined fertilizers, theoretically derived EC prediction methods are required. In this study, the experimental EC prediction equation, an extended form of the Robinson and Stroke's theoretical equation only available for a binary electrolyte, was developed for predicting the EC of the nutrient solution containing many kinds of inorganic compounds. And the multilayer perceptron consisting of three layers with the back propagation learning algorithm was developed for EC prediction. It consists of nine variables in the input layer for the concentrations of seven macro elements, Na+ and Cl, and one variable in the output layer for the EC of nutrient solution. The predicted ECs by experimental model as well as neural networks for the nutrient solution were compared to the measured ones and showed good agreements.

Culture Media↗

PubMed2004

Neural networks for protein classification.

This paper describes a biomolecular classification methodology based on multilayer perceptron neural networks. The system developed is used to classify enzymes found in the Protein Data Bank. The primary goal of classification, here, is to infer the function of an (unknown) enzyme by analysing its structural similarity to a given family of enzymes. A new codification scheme was devised to convert the primary structure of enzymes into a real-valued vector. The system was tested with a different number of neural networks, training set sizes and training epochs. For all experiments, the proposed system achieved a higher accuracy rate when compared with profile hidden Markov models. Results demonstrated the robustness of this approach and the possibility of implementing fast and efficient biomolecular classification using neural networks.

Algorithms↗

PubMed1995

Computational intelligence for laboratory information systems.

Non-linear models, such as given by neural networks and fuzzy logic, have established a good reputation for medical data analysis as computational and logical counterparts to statistical methods. Whereas multilayer perceptrons perform well with large data sets, a combination of neural learning together with fuzzy logical network interpretations provides a network reduction well suited for smaller data sets. The aim of this paper is to present an approach to neural fuzzy systems data analysis and knowledge acquisition in laboratory information systems. We also describe a software system, DiagaiD, which provides an analysis and development workbench involving laboratory data.

Clinical Laboratory Information Systems↗

PubMedDate not supplied

Computer image analysis of brain CT images for discriminating hypodense cerebral lesions in children.

A computer software system was designed for the automatic discrimination of focal oedemas from local glioses in brain CT examinations. Image analysis methods were applied to the images of 77 CT examinations of children with focal oedemas (42) or local glioses (35). Textural features derived from the co-occurrence matrix of the lesion's image and a neural network classifier (the multilayer perceptron) were employed for the design of the system. Best classification accuracy (89.6%) was achieved by two textural features (contrast-difference entropy), one hidden layer and three hidden nodes of the classifier. The proposed software system provides new textural information and may be of value to the radiologist in differentiating focal oedemas from local glioses, especially in small lesions, where other radiological criteria are not evident.

Brain↗

PubMed1997

EP parametrization and classification using wavelet networks--theoretical concept and medical application.

The purpose of this paper is to outline the fundamental concept of wavelet networks (WNs) and to demonstrate its specific advantages in a clinical discrimination task. A group of 25 boys with attention deficit hyperactivity disorder (ADHD) should be separated from a control group of 25 healthy boys by auditory evoked potentials. Because of the high variability of the recorded EP signals quantification of the averaged sweeps by peak latencies and amplitudes failed. However, with wavelet networks a maximum classification rate of 80% was achieved by crossvalidation. A WN is basically described as a multilayer perceptron which consists of two parts for feature extraction/parametrization and classification. These essential steps of a pattern recognition task are not separated in different tasks but linked together by the clamp of the learning algorithm. Because no user interaction is necessary we call this procedure a self-learning method.

Arousal↗

PubMedDate not supplied

[Classification of cytological smears of the cervix with neuronal methods].

BACKGROUND: Cytological smears obtained from the cervix are routinely examined under the microscope as part of screening programs for the early detection of cervical cancer. The aim of the present study was to investigate whether a simple feature extraction approach using only standard image processing techniques combined with a neural classifier would lead to acceptable results that might serve as a starting point for the development of a fully automated screening system. MATERIALS AND METHODS: Gray-value images of 106 cervical smears (512 x 512 pixels) divided into two groups--inconspicuous (57) and atypical (49)--by an experienced pathologist on the basis of the original smears were employed to evaluate the method. From these images, 31 features quantifying properties of either the cell nucleus or the cytoplasm were extracted. These features were categorized with three different architectures of a neural classifier: learning vector quantization (LVQ), multilayer perceptron (MLP) and a single perceptron. CONCLUSIONS: The results show a reclassification accuracy of about 91% for all three algorithms. Sensitivity was uniform at approximately 78%, and specificity varied between 75% and 91% in the leave-one-out evaluation. These very good results provide strong encouragement for further studies involving PAP scores and colour images.

Artificial Intelligence↗