A Framework for Event Classification from Video Sequences using Bayesian Neural Network

Female breast cancer is a major cause of deaths in occidental countries. Computer-aided Detection (CAD) systems can aid radiologists to increase diagnostic accuracy. In this work, we present a comparison between two classifiers applied to the separation of normal and abnormal breast tissues from mammograms. The purpose of the comparison is to select the best prediction technique to be part of a CAD system. Each region of interest is classified through a Support Vector Machine (SVM) and a Bayesian Neural Network (BNN) as normal or abnormal region. SVM is a machine-learning method, based on the principle of structural risk minimization, which shows good performance when applied to data outside the training set. A Bayesian Neural Network is a classifier that joins traditional neural networks theory and Bayesian inference. We use a set of measures obtained by the application of the semivariogram, semimadogram, covariogram, and correlogram functions to the characterization of breast tissue as normal or abnormal. The results show that SVM presents best performance for the classification of breast tissues in mammographic images. The tests indicate that SVM has more generalization power than the BNN classifier. BNN has a sensibility of 76.19% and a specificity of 79.31%, while SVM presents a sensibility of 74.07% and a specificity of 98.77%. The accuracy rate for tests is 78.70% and 92.59% for BNN and SVM, respectively.

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Using Bayesian Neural Network for Predicting Fatigue Strength Based on Composition and Process Parameters

10.1115/1.0001246v ◽

2021 ◽

Author(s):

R M Chandima Ratnayake ◽

Arvind Keprate

Keyword(s):

Neural Network ◽

Fatigue Strength ◽

Process Parameters ◽

Bayesian Neural Network ◽

Strength Based

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Bayesian Neural Network Applied in Medical Survival Analysis of Primary Biliary Cirrhosis

2012 UKSim 14th International Conference on Computer Modelling and Simulation ◽

10.1109/uksim.2012.20 ◽

2012 ◽

Cited By ~ 1

Author(s):

Corneliu T.C. Arsene ◽

Paulo J. Lisboa

Keyword(s):

Neural Network ◽

Survival Analysis ◽

Primary Biliary Cirrhosis ◽

Biliary Cirrhosis ◽

Bayesian Neural Network

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A Bayesian neural network predicts the dissolution of compact planetary systems

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2026053118 ◽

2021 ◽

Vol 118 (40) ◽

pp. e2026053118

Author(s):

Miles Cranmer ◽

Daniel Tamayo ◽

Hanno Rein ◽

Peter Battaglia ◽

Samuel Hadden ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Time Series ◽

Planetary System ◽

Machine Learning Algorithms ◽

Orbital Elements ◽

Bayesian Neural Network ◽

Inference Model ◽

Link Type ◽

Numerical Integrator

We introduce a Bayesian neural network model that can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both nonresonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to 105 times faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK (https://github.com/dtamayo/spock) package, with training code open sourced (https://github.com/MilesCranmer/bnn_chaos_model).

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