A new approach for Pap-Smear image generation with generative adversarial networks
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Purpose: In this study, a new GAN model for histopathological Pap-Smear images generation is suggested. To illustrate the advantage of the proposed GAN model (Pix2PixSSIM), a comprehensive experimental study has been carried out. Theory and Methods: Pix2PixSSIM designed as generative adversarial networks model for histopathological Pap-Smear images generation. In addition, the proposed model compared with the existing GANs (i.e., Pix2Pix, CycleGAN, DiscoGAN and AttentionGAN). Results: In experimental studies, Pix2PixSSIM, which is designed as generative adversarial networks model for histopathological Pap-Smear images generation, has shown high accuracy than other methods. Conclusion: It is seen that the performance of the proposed GAN architecture to produce patterns similar to real Pap-Smear visuals gives successful results (MSI=23.649, PSNR=37.476) when compared to existing approaches (MathModel and classical image synthesis methods).
Açıklama
Anahtar Kelimeler
Pap-Smear, Image generation, Deep learning, Convolutional neural network, Generative Adversarial Networks
Kaynak
Journal of The Faculty of Engineering and Architecture of Gazi University
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
37
Sayı
3
