Development of Easyget Algorithm for Deep Learning Sculptor Deepcnet Model using Hadoop Architecture
Jyoti S.Patil1, G.Pradeepini2
1Ms.Jyoti Patil*, Ph. D. Research Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P. India.
2Dr.G.Pradeepini, Professor, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, A.P. India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 30, 2020. | Manuscript published on April 10, 2020. | PP: 1646-1650 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4327049620/2020©BEIESP | DOI: 10.35940/ijitee.F4327.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Manual segmentation in the brain tumors analyses for malignancy prognosis, via massive amount MRI images produced through medical routine, frustrating task and is a hard. There is a dependence on automated brain tumor graphic segmentation. The amount of precision necessary for scientific purposes is normally as yet not known, and so can’t be conveniently quantified actually by means of professional physicians. That is a fascinating point, which includes just sparsely been resolved in the literature, but is nonetheless truly relevant up to now. Additionally, storage space automatization for medical images is essential need nowadays. To carry out very quickly analysis as well as, prognosis there’s an imperative want of automated photo storage. Hence, this paper focused on development of new algorithm called “EasyGet” for automatic data storage and retrieval using Hadoop architecture.
Keywords: Deep learning, Big data, Hadoop storage, Machine learning
Scope of the Article: Deep learning