A new approach for Pap-Smear image generation with generative adversarial networks

Altun, SaraTalu, Muhammed Fatih2024-08-042024-08-0420221300-18841304-4915https://doi.org/10.17341/gazimmfd.939092https://search.trdizin.gov.tr/yayin/detay/508632https://hdl.handle.net/11616/92644Purpose: 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).eninfo:eu-repo/semantics/openAccessPap-SmearImage generationDeep learningConvolutional neural networkGenerative Adversarial NetworksA new approach for Pap-Smear image generation with generative adversarial networksArticle3731401141010.17341/gazimmfd.9390922-s2.0-85128740867Q2508632WOS:000834843300020Q4