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

Renganathan, V (2022) Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software. Serbian Journal of Experimental and Clinical Research, 23 (1). pp. 3-11. ISSN 1820-8665

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With the increase in volume of data and presence of structured and unstructured data in the biomedical fi led, there is a need for building models which can handle complex & non-linear re-lations 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 fi led in the recent years. Deep learning methodology evolved from artifi cial neural net-works which process the input data through multiple hidden lay-ers with higher level of abstraction. Deep Learning networks are used in various fi elds such as image processing, speech recogni-tion, fraud deduction, classifi cation 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 over-view of Deep Learning Models, its types, its application in bio-medical 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

Affiliation: Skyline University College
SUC Author(s): Renganathan, V
All Author(s): Renganathan, V
Item Type: Article
Uncontrolled Keywords: Deep learning network, Convolution network, Clas-sifi cation, image processing, Artifi cial Neural Network.
Subjects: B Information Technology > BR Deep Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr SUC Library
Date Deposited: 24 Jun 2022 17:49
Last Modified: 24 Jun 2022 17:49
URI: https://research.skylineuniversity.ac.ae/id/eprint/429
Publisher URL: https://doi.org/10.2478/sjecr-2018-0063
Publisher OA policy: https://v2.sherpa.ac.uk/id/publication/27076
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