Empowerment of Big Data and Eco-System in Diabetes Retinopathy [Retinal Images] - A Review

Aasha Dhanapal*, Sabibullah Mohamed Hanifa**
* Research Scholar, PG & Research Department of Computer Science, Sudharsan College of Arts & Science (SCAS) , PudukkottaI, Tamil Nadu, India.
** Associate Professor & Dean, PG & Research Department of Computer Science, Sudharsan College of Arts & Science (SCAS), Pudukkottai, Tamil Nadu, India.
Periodicity:March - May'2017
DOI : https://doi.org/10.26634/jpr.4.1.13644

Abstract

Many individuals are not interested in health and find self-tracking to be an alien concept. Health is still perceived as the responsibility of physicians, and health-related information is thought to be deterministic, negative, and unwanted. Ophthalmic Research has given better understanding of the “sight-threatening disease / vision loss” processes, is now opening up new avenues in the line of prevention and treatment. Diabetes Retinopathy (DR) only affects people, who have diabetes for a longer period of time and result in blindness or loss of vision. This review would certainly focus on importance behind the research motivation on DR’s early intervention, its niceties, role, and potential use of Big Data Analytics (Strategic Technology). Hadoop's core components [like HDFS (HIPI-Hadoop Image Processing Interface - Executable Algorithms), MapReduce and Yet Another Resource Negotiar (YARN) and its Ecosystem (Hive, Hbase, Pig, etc.,) are employed everywhere in the corners of medical domain. Since, Healthcare constituents and researchers are being impacted by big data arrival, from which better treatment efficiency in the prediction task on DR would be possible by these players.

Keywords

Ophthalmic Research, Diabetes Retinopathy, Healthcare Ingredients, Big Data, HIPI, MapReduce, Retinal Images, Risk Factors, Hadoop Core, Eco-System

How to Cite this Article?

Dhanapal, A., and Hanifa, S. M. (2017). Empowerment of Big Data and Eco-System in Diabetes Retinopathy [Retinal Images] - A Review. i-manager’s Journal on Pattern Recognition, 4(1), 36-43. https://doi.org/10.26634/jpr.4.1.13644

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