<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the increase in volume of data and presence of structured and unstructured data in the biomedical filed, there is a need for building models which can handle complex &amp; non-linear relations in the data and also predict and classify outcomes with higher accuracy. Deep learning models are one of such models which can handle complex and nonlinear data and are being increasingly used in the biomedical filed in the recent years. Deep learning methodology evolved from artificial neural networks which process the input data through multiple hidden layers with higher level of abstraction. Deep Learning networks are used in various fields such as image processing, speech recognition, fraud deduction, classification and prediction. Objectives of this paper is to provide an overview of Deep Learning Models and its application in the biomedical domain using R Statistical software Deep Learning concepts are illustrated by using the R statistical software package. X-ray Images from NIH datasets used to explain the prediction accuracy of the deep learning models. Deep Learning models helped to classify the outcomes under study with 91% accuracy. The paper provided an overview of Deep Learning Models, its types, its application in biomedical domain. - is paper has shown the effect of deep learning network in classifying images into normal and disease with 91% accuracy with help of the R statistical package.</p>
</abstract>

With the increase in volume of data and presence of structured and unstructured data in the biomedical filed, there is a need for building models which can handle complex & non-linear relations in the data and also predict and classify outcomes with higher accuracy. Deep learning models are one of such models which can handle complex and nonlinear data and are being increasingly used in the biomedical filed in the recent years. Deep learning methodology evolved from artificial neural networks which process the input data through multiple hidden layers with higher level of abstraction. Deep Learning networks are used in various fields such as image processing, speech recognition, fraud deduction, classification and prediction. Objectives of this paper is to provide an overview of Deep Learning Models and its application in the biomedical domain using R Statistical software Deep Learning concepts are illustrated by using the R statistical software package. X-ray Images from NIH datasets used to explain the prediction accuracy of the deep learning models. Deep Learning models helped to classify the outcomes under study with 91% accuracy. The paper provided an overview of Deep Learning Models, its types, its application in biomedical domain. - is paper has shown the effect of deep learning network in classifying images into normal and disease with 91% accuracy with help of the R statistical package.

Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software

Experimental and Applied Biomedical Research (EABR)

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

With the increase in volume of data and presence of structured and unstructured data in the biomedical filed, there is a need for building models which can...

Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software

Pubblicato online: 07 apr 2022

Pagine: 3 - 11

Ricevuto: 18 lug 2018

Accettato: 12 ott 2018

DOI: https://doi.org/10.2478/sjecr-2018-0063

Parole chiave
Deep learning network, Convolution network, Classification, image processing, Artificial Neural Network

© 2022 Vinaitheerthan Renganathan, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software

Pubblicato online: 07 apr 2022

Pagine: 3 - 11

Ricevuto: 18 lug 2018

Accettato: 12 ott 2018

DOI: https://doi.org/10.2478/sjecr-2018-0063

Parole chiaveDeep learning network, Convolution network, Classification, image processing, Artificial Neural Network

© 2022 Vinaitheerthan Renganathan, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Deep learning network, Convolution network, Classification, image processing, Artificial Neural Network