kaz44Вестник Университета Шакарима. Серия технические наукиBulletin of Shakarim University. Technical Sciences2788-79953006-0524«Шәкәрім университеті» КеАҚ10.53360/2788-7995-2025-4(20)-11kaz44-2144Research ArticleАВТОМАТИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ (ОРИГИНАЛЬНАЯ СТАТЬЯ)AUTOMATION AND INFORMATION TECHNOLOGY (ORIGINAL ARTICLE)ОЦЕНКА STYLEGAN2 И STYLEGAN3 ДЛЯ СИНТЕТИЧЕСКОЙ ГЕНЕРАЦИИ МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ НА ДАТАСЕТАХ BUSI И CBIS-DDSMEVALUATION OF STYLEGAN2 AND STYLEGAN3 FOR SYNTHETIC MEDICAL IMAGE GENERATION ON BUSI AND CBIS-DDSM DATASETShttps://orcid.org/0000-0001-5055-4149РыспаеваМ. К.RyspayevaM. K.

Марья Куанышевна Рыспаева, докторант; преподаватель

110000, Костанай, Казахстан, ул. Байтурсынова 47 

010000, Астана, Казахстан Мангилик Ел, блок C.1 

Marya Ryspayeva – doctoral student; lecturer

Baytursynov Street 47, 110000, Kostanay, KazakhstanMangilik El, block C.1, 010000, Astana, Kazakhstan

marya.rys1@mail.ruhttps://orcid.org/0000-0002-8681-4552СалыковаО. С.SalykovaS. O.

Ольга Салыкова – кандидат технических наук, и.о. ассоциированного профессора

110000, Костанай, Казахстан, ул. Байтурсынова 47 

 

Olga Salykova – candidate of technical sciences, associate professor

Baytursynov Street 47, 110000, Kostanay, Kazakhstan 

solga0603@mail.ruКостанайский региональный университет имени Ахмета Байтурсынулы; Astana IT UniversityКазахстанAkhmet Baitursynuly Kostanay Regional University; Astana IT UniversityKazakhstanКостанайский региональный университет имени Ахмета БайтурсынулыКазахстанAkhmet Baitursynuly Kostanay Regional UniversityKazakhstan20252501202614(20)8996Copyright © Рыспаева М.К., Салыкова О.С., 20262026Рыспаева М.К., Салыкова О.С.Ryspayeva M.K., Salykova S.O.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://tech.vestnik.shakarim.kz/jour/article/view/2144

Глубокое обучение на медицинских изображениях обычно затруднено ограниченным доступом к данным и сильным дисбалансом классов, что снижает эффективность традиционных алгоритмов машинного обучения. Генеративные состязательные сети (GAN) могут использоваться для решения таких проблем путем создания реалистичных синтетических изображений, дополняющих обучающие выборки. В данном исследовании мы сравниваем две передовые архитектуры GAN, StyleGAN2 и StyleGAN3, на основе двух общедоступных наборов данных изображений молочной железы: BUSI (ультразвук, 210 случаев злокачественных опухолей) и CBIS-DDSM (маммография, 509 случаев злокачественных опухолей). Оценка проводилась с использованием метрик FID и KID. На BUSI StyleGAN3 при 1000 эпохах достиг FID = 140.7 и KID = 0.06, тогда как StyleGAN2 показал FID = 259.7 и KID = 0.25. На CBIS-DDSM StyleGAN3 достиг FID = 90.6 и KID = 0.06, а StyleGAN2 – FID = 124.8 и KID = 0.10. Эти результаты демонстрируют, что StyleGAN3 имеет тенденцию синтезировать более реалистичные и разнообразные изображения в условиях ограниченных данных, но при этом требует большего времени обучения, тогда как StyleGAN2 обеспечивает сопоставимое качество при меньших вычислительных затратах. Результаты указывают на потенциал генерации медицинских изображений и компромисс между качеством и эффективностью для задач аугментации данных при улучшении изображений молочной железы.

Deep learning from medical images is typically hindered by limited access to images and severe imbalance of classes that reduces the effectiveness of typical machine learning algorithms. Generative adversarial networks can be employed to address such issues by creating natural-appearing synthetic images to complement training sets. In this study, we compare two advanced GAN architectures, StyleGAN2 and StyleGAN3, using two publicly available breast imaging datasets: BUSI (ultrasound, 210 malignant cases) and CBIS-DDSM (mammography, 509 malignant cases). Evaluation was based on Fréchet Inception Distance and Kernel Inception Distance. On BUSI, StyleGAN3 achieved FID = 140.7 and KID = 0.06 at 1000 epochs, whereas StyleGAN2 achieved FID = 259.7 and KID = 0.25. On CBIS-DDSM, StyleGAN3 achieved FID = 90.6 and KID = 0.06, and StyleGAN2 achieved FID = 124.8 and KID = 0.10. These results demonstrate that StyleGAN3 has a tendency to synthesize images that are more natural and diversified under limited dataset conditions, at the cost of increased training times, whereas StyleGAN2 provides similar quality at less expensive computational costs. The results indicate the potential of generating medical images and the tradeoff between image quality and efficiency in data augmentation for breast cancer image improvement.

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The authors declare that there are no conflicts of interest present.