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Register: Jurnal Ilmiah Teknologi Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 25030477     EISSN : 25023357     DOI : https://doi.org/10.26594/register
Core Subject : Science,
Computer Science & IT
Register: Scientific Journals of Information System Technology is an international, peer-reviewed journal that publishes the latest research results in Information and Communication Technology (ICT). The journal covers a wide range of topics, including Enterprise Systems, Information Systems Management, Data Acquisition and Information Dissemination, Data Engineering and Business Intelligence, and IT Infrastructure and Security. The journal has been indexed on Scopus (reputated international indexed) and accredited with grade “SINTA 1” by the Director Decree (1438/E5/DT.05.00/2024) as a recognition of its excellent quality in management and publication for international indexed journal.
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Articles 1 Documents
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Search results for , issue "Vol 12 No 1 (2026): January (In Progress)" : 1 Documents clear
Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP) Diyasa, I Gede Susrama Mas; Kuswardhani , Hajjar Ayu Cahyani; Idhom, Mohammad; Riyantoko, Prismahardi Aji; Dewi , Deshinta Arrova
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5954

Abstract

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.

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