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All Journal Jurnal Penelitian Jifosi International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Computer and Information System (J-CIS) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
I Gede Susrama Mas Diyasa
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Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Transportation

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Image Synthesis for Sperm Dataset Augmentation using WGAN-GP Hajjar Ayu Cahyani Kuswardhani; I Gede Susrama Mas Diyasa; Mohammad Idhom
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.146

Abstract

This research explores the efficacy and limitations of applying a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to generate synthetic human sperm microscopy images for data augmentation. We assessed the WGAN-GP's performance on a complex, heterogeneous dataset where images contained multiple object types. Despite achieving stable training convergence, the model's output quality was suboptimal, as evidenced by a high Fréchet Inception Distance (FID) score of 134 and qualitative signs of partial mode collapse. The generator struggled to capture the complete morphological diversity of the sperm cells. A second experiment using a dataset pre-sorted into distinct classes (Normal, Abnormal, Non-Sperm) yielded a marked improvement. This approach led to substantially lower FID scores (59.19, 74.92, and 83.56) and exhibited more robust training dynamics. Our findings underscore a critical conclusion: the success of WGAN-GP in this domain is fundamentally tied to the simplicity of the data distribution. We recommend that future efforts leverage class-conditioned models, simplified data structures, and refined generator architectures to achieve high-precision augmentation for medical imaging tasks.
Co-Authors Agung Mustika Rizki Akbar, Fawwaz Ali Ani Dijah Rahajoe Ariyono Setiawan Di Asih I Maruddani Dian Agus Prawinata Dimas Saputra Eva Yulia Puspaningrum Fityan Hanif Assalmi hanif Hadiansyah Rachmawan Putra Hajjar Ayu Cahyani Kuswardhani Haq, Adelia Azizatul Ida Ayu Putu Sri Widnyani M. Hafidz Amarul Ma'rufi Michael Alexander Justin Audison Sibarani Mohammad Idhom Prisma Hardi Aji Riyantoko Putra, Chrystia Aji Rico Sandyca Novenza Sri Wibawani Sugiarto Trimono, Trimono Wan Suryani Wan Awang