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

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