Important Links
Call for Papers

call for paper IJCRAS
Submit Article IJCRAS

Why us?
  • Open Access
  • Peer Reviewed
  • Rapid Publication
  • Life time hosting
  • Free promotion service
  • Free indexing service
  • More citations
  • Search engine friendly
Subject Area
  • Life Sciences / Biology
  • Architecture / Building
  • Management
  • Asian Studies
  • Business & Management
  • Chemistry
  • Computer Science
  • Economics & Finance
  • Engineering / Acoustics
  • Environmental Science
  • Agricultural Sciences
  • Pharmaceutical Sciences
  • General Sciences
  • Materials Science
  • Mathematics
  • Medicine
  • Nanotechnology &
  • Nanoscience
  • Nonlinear Science
  • Chaos & Dynamical
  • Systems
  • Physics
  • Social Sciences &
  • Humanities

Authors: K. Shayam Kishore and Y Sravani Devi

Abstract: The amount of data, particularly in medical imaging, is one of the most important factors in classifying the image. Nevertheless, obtaining the datasets is the biggest obstacle in the healthcare industry. In this, we prepare a VAE (variational autoencoder) and another model known as the DCGAN (deep convolutional generative adversarial networks), on almost 3662 retinal images that have been captured from a dataset known as the APTOS- Blindness dataset, to display the images of the synthesized retinal fundus. The advantage of this method is that retinal pictures can be produced without the preceding vessel segmentation technique. As a result, the system can become autonomous. The models that are acquired are the image synthesizers that are adept at synthesizing resized retinal images of any amount from a fundamentally regular distribution. Furthermore, more images than this have been used in literature for training purposes than for any other endeavor. Giving an output to a CNN model allows for the evaluation or appraisal of a synthetic image, and the average squared error between the average 2-dimensional hologram of actual and synthetic images was also calculated. by examining the average loss and latent space of the images later. The analysis’s successful results suggested that DCGAN, as opposed to Variational Auto Encoders, has less loss in general images.

Keywords:  Data Augmentation, DC-GAN, Variational Auto Encoder (VAE), Diabetic Retinopathy, Generative Adversarial Networks, CNN.

PDF Download
Author Information
  • Rapid Publishing
  • Professional publishing practices
  • Indexing in leading database
  • High level of citation
  • High-Quality reader base
  • High-level author support

IJCRAS is following an instant policy on rejecting received papers with a plagiarism rate of more than 20%. So, All authors and contributors must check their papers before submission to make assurance of following our anti-plagiarism policies.

Modes of Payment


Bank Account Transfer

Western Union IJCRAS