Artificial neural networks in medical diagnosis

Amato, Filippo; López, Alberto Botana; Peña‐Méndez, Eladia María; Vaňhara, Petr; Hampl, Aleš; Havel, Josef

doi:10.2478/v10136-012-0031-x

Cited by 734 publications

(412 citation statements)

References 49 publications

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“…Various transfer functions are available, however the most commonly used is the sigmoid function [12]: In this study, MCNPX (version 2.6.0) has been used as the Monte Carlo code. MCNPX is a general purpose radiation transport code for modeling the interaction of radiation with materials, which also tracks all particles at all energies.…”

Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

et al. 2017

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Bremsstrahlung has an important place in the field of experimental physics, especially for description of photon-matter interaction and for characterization and analysis of materials. Bremsstrahlung photon is created by a high-energy electron, deflected in the electric field of atomic nucleus. Bremsstrahlung is also important for experimental studies, not only in the field of nuclear physics and particle physics but also in the fields of solid state physics, applied physics and astrophysics. In recent years, Monte Carlo simulation has become a widely used method for calculations related to bremsstrahlung. On the other hand, predictions by using artificial neural network can be performed with high accuracy. This study aims at observing variation in the photon flux as unction of target thickness and at processing output data by using an artificial neural network. We achieved a high degree of compatibility between two different methods. This study suggests that artificial neural network is a powerful tool for prediction of Bremsstrahlung and for other scientific problems.

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Section: Methodsmentioning

confidence: 99%

An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

et al. 2017

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“…Due to its learning and generalization capabilities, neural networks can be expressed as a mathematical representation of the human neural architecture [37]. In this study, the terms neural network and artificial neural network are used interchangeably.…”

Section: Neural Networkmentioning

confidence: 99%

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Şahin

Erol

2017

MCA

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Abstract:The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

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“…The multilayer perceptron (MLP) is one of the most popular supervised ANN models due to its architecture and simple algorithm [40]. A MLP consists on an input layer that receives the values of the variables, one or more hidden layers where the adjustment processes are carried out and the output layer [16,41]. For the construction of the architecture of our MLP (Fig.…”

Section: Aid Decision Algorithms 321 Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Aid decision algorithms to estimate the risk in congenital heart surgery

Ruiz-Fernández

Torra

Soriano-Payá

et al. 2016

Computer Methods and Programs in Biomedicine

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A c c e p t e d M a n u s c r i p t Highlights  We propose an alternative system for classifying the risk in paediatric congenital heart surgery. Four methods are tested: a perceptron multilayer, self-organising maps, a radial basis function neural network and decision trees. We obtain an accuracy of 99.87% (using pre and post-surgical data) and 83% (using just pre-surgical data).Page 2 In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. MethodsWe have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. ResultsAccuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. ConclusionsAccording to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

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Artificial neural networks in medical diagnosis

Cited by 734 publications

References 49 publications

An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Aid decision algorithms to estimate the risk in congenital heart surgery

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