A Comparative Study of Generative Adversarial Networks in Medical Image Processing

The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN architectures, Pix2Pix, SPADE GAN, and Wasserstein GAN...

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Vydané v:Eng (Basel, Switzerland) Ročník 6; číslo 11; s. 291
Hlavní autori: Abdulqader, Marwa Mahfodh, Abdulazeez, Adnan Mohsin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2025
Predmet:
Accuracy
Brain
Brain cancer
Comparative studies
Computer vision
Datasets
deep learning
Generative adversarial networks
Image processing
Image segmentation
Magnetic resonance imaging
medical image
Medical imaging
MRI
Realism
segmentation
Signal to noise ratio
Synthesis
Task complexity
Tumors
ISSN:2673-4117, 2673-4117
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Shrnutí:The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and restoration. This study presents a comparative evaluation of three representative GAN architectures, Pix2Pix, SPADE GAN, and Wasserstein GAN (WGAN), across multiple medical imaging tasks, including segmentation, image synthesis, and enhancement. Experiments were conducted on three benchmark datasets: ACDC (cardiac MRI), Brain Tumor MRI, and CHAOS (abdominal MRI). Model performance was assessed using Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Dice coefficient, and segmentation accuracy. Results show that SPADE-inpainting achieved the best image fidelity (PSNR ≈ 36 dB, SSIM > 0.97, Dice ≈ 0.94, FID < 0.01), while Pix2Pix delivered the highest segmentation accuracy (Dice ≈ 0.90 on ACDC). WGAN provided stable enhancement and strong visual sharpness on smaller datasets such as Brain Tumor MRI. The findings confirm that no single GAN architecture universally excels across all tasks; performance depends on data complexity and task objectives. Overall, GANs demonstrate strong potential for medical image augmentation and synthesis, though their clinical utility remains dependent on anatomical fidelity and dataset diversity.
Bibliografia:ObjectType-Article-1
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ISSN:2673-4117
2673-4117
DOI:10.3390/eng6110291