International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Creating High Resolution Images using Incremental DCGAN

Author(s) B. Sateesh, Dr M. Swamy Das
Country India
Abstract Deep Learning in the field of Medical Sciences is one of the most demanding areas nowadays. There are many types of medical images like x-rays, CT scans, MRI scans, ultra sound images etc. X-rays are very low cost, easy to produce and fast to diagnosis the abnormality [2]. Till today Radiologists are manually interpreting the X-rays. Many researchers contributed their work to increase the accuracy of classification of medical images like X-rays. But still there is high scope to improve accuracy of medical image classification. One of the biggest challenge is to get datasets to perform experiments. Because of law of privacy of patients, it's not an easy thing to get inputs from medical laboratories. Though there are datasets in public domains. Still there is scope to investigate new data augmentation techniques to produce high quality synthetic data. In this work Incremental Deep Convolution Generative Adversarial Network (IDCGAN) is implemented. It is based on Deep Convolution GAN [2]. The model learns to produce high quality images in an incremental manner. It starts to learn from 64 x 64 resolution. Later it is trained to produce high resolution images. The experimental results showed high resolution Chest X-ray images with resolution 2048 x 2048.
Keywords DCGAN, Generator, Discriminatro
Field Computer Applications
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-23
Cite This Creating High Resolution Images using Incremental DCGAN - B. Sateesh, Dr M. Swamy Das - IJFMR Volume 5, Issue 5, September-October 2023.
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com