JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.37, sa.3, ss.1401-1410, 2022 (SCI-Expanded)
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).