Generating Realistic Blood-Cell Images using Cycle-Consistent Generative Adversial Networks
M. V. Nageswara Rao
M. V. Nageswara Rao, Electronics and Communication Engineering, GMRIT, Rajam, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2160-2161 | Volume-8 Issue-12, October 2019. | Retrieval Number: L29481081219/2019©BEIESP | DOI: 10.35940/ijitee.L2948.1081219
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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: Generative adversial networks are a neural-network based generative models , predominantly used for generating data-samples close to the data distribution they have been trained on. A model for generating realistic blood cell images based on cycle-consistent generative adversial networks is developed along with their corresponding segmentation masks.
Keywords: GAN, Cycle-Consistency
Scope of the Article: Communication Engineering