Image Analysis using Deep Learning Techniques
P. Jayasri1, N. Subhash Chandra2
1P. Jayasri, PG Scholar, Department of CSE, CVRCE, Hyderabad (Telangana), India.
2Dr. N. Subhash Chandra, Professor, Department of CSE, CVRCE, Hyderabad (Telangana), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1385-1388 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F12460986S319/19©BEIESP | DOI: 10.35940/ijeat.F1246.0986S319
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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: In late years, critical learning methodologies especially Convolutional Neural Networks have been utilized in different solicitations. CNN’s have appeared to be a key capacity to ordinarily expel broad volumes of data from massive information. The uses of CNNs have inside and out ended up being useful especially in orchestrating ordinary pictures. Regardless, there have been essential obstacles in executing the CNNs in a restorative zone as a result of the nonattendance of genuine getting ready data. Consequently, general imaging benchmarks, for instance, Image Net have been conspicuously used in the restorative not too zone notwithstanding the way that they are perfect when appeared differently about the CNNs. In this paper, a comparative examination of LeNet, Alex Net, and GoogLe Net has been done. Starting there, the paper has proposed an improved hypothetical structure for requesting helpful life structures pictures using CNNs. In perspective on the proposed structure of the framework, the CNNs building are required to beat the previous three plans in requesting remedial pictures.
Keywords: Convolutional Neural Networks, ImageNet, Alex Net, and GoogLe Net.
Scope of the Article: Deep Learning