Granularity based image processing Eco system in Hadoop to predict the detailed results for different medical images

Abstract

Hadoop has become true industry standard for kernel of the distributed operating system in Big data. Hadoop has picked up its prevalence because of its capacity of processing huge data, breaking down and getting to substantial measure of information, rapidly and also it is more stable technology. “Hadoop” has HDFS, YARN and Map Reduceas its core components. To supplement the Hadoop modules there are additionally an assortment of different components that give particular administrations and are extensively used to make Hadoop more available and more usable which is known as Hadoop Ecosystem. Late HadoopEco system comprises of various layers, each layer performing different kind of tasks like storing your data, processing stored data, resource allocating and supporting different programming languages to develop various applications. Granular Computing (GrC) can be considered as a common name of theories, methodologies, techniques and tools that make use of granules, i.e. groups, classes, or clusters of a universe, in the process of solving problems. Basic ideas of crisp information granulation have appeared in related fields. This paper proposes a new Ecosystem for Hadoop which can process date in image format and analyze to give proper results.