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All Journal Jurnal Simantec JIEET (Journal of Information Engineering and Educational Technology) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences ITIJ
Mas Diyasa, I Gede Susrama
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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Improving Classification Accuracy of Breast Ultrasound Images Using Wasserstein GAN for Synthetic Data Augmentation Mas Diyasa, I Gede Susrama; Humairah, Sayyidah; Puspaningrum, Eva Yulia; Durry, Fara Disa; Lestari, Wahyu Dwi; Caesarendra, Wahyu; Dewi, Deshinta Arrova; Aryananda, Rangga Laksana
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.25075

Abstract

Breast cancer remains one of the most prevalent cancers in Indonesia, and early detection plays a vital role in improving patient outcomes. Ultrasound imaging is a non-invasive and accessible technique used to classify breast conditions into normal, benign, or malignant categories. The advancement of deep learning, particularly Transfer Learning with Convolutional Neural Networks (CNNs), has significantly enhanced the performance of automated image classification. However, the effectiveness of CNNs heavily relies on large, balanced datasets—resources that are often limited and imbalanced in medical domains. To address this issue, this study explores the use of Wasserstein Generative Adversarial Networks (WGAN) for synthetic data augmentation. WGAN is capable of learning the underlying distribution of real ultrasound images and generating high-quality synthetic samples. The inclusion of the Wasserstein distance stabilizes training, with convergence observed around 2500–3000 epochs out of 5000. While synthetic data improves classifier performance, there remains a potential risk of overfitting, particularly when the synthetic images closely mirror the training data. Compared to traditional augmentation techniques such as rotation, flipping, and scaling, WGAN-generated data provides more diverse and realistic representations. Among the tested models, VGG16 achieved the highest accuracy of 83.33% after WGAN augmentation. Nonetheless, computational resource limitations posed challenges in training stability and duration. Furthermore, issues related to model generalizability, as well as ethical and patient privacy considerations in using synthetic medical data, must be addressed to ensure responsible deployment in real-world clinical settings.
Co-Authors Achmad Junaidi Adiwidyatma, Afdhal Reshanda Akmal, Mohammad Faizal Alhamda, Denisa Septalian Amanullah , Nurkholis Ardianto, Taruna Aryananda, Rangga Laksana Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Gunawan, Ellexia Leonie Hadi, Surjo Halim, Christina Humairah, Sayyidah Jauharis Saputra, Wahyu Syaifullah Mumtaz, Ahmad Naufal Mustika, Agung Nadhira, Firya Santoso, Sri Fuji Setiawan, Ariyono Sri Wibawani, Sri Sugiarto S Sukri, Hanifudin Terza Damaliana, Aviolla Trimono, Trimono Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wan Awang, Wan Suryani Wijaya, Pandu Ali Yisti Vita Via