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...

Full description

Saved in:

Bibliographic Details
Published in:	Eng (Basel, Switzerland) Vol. 6; no. 11; p. 291
Main Authors:	Abdulqader, Marwa Mahfodh, Abdulazeez, Adnan Mohsin
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2025
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	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.
AbstractList	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.
Author	Abdulazeez, Adnan Mohsin Abdulqader, Marwa Mahfodh
Author_xml	– sequence: 1 givenname: Marwa Mahfodh surname: Abdulqader fullname: Abdulqader, Marwa Mahfodh – sequence: 2 givenname: Adnan Mohsin orcidid: 0000-0002-4357-7331 surname: Abdulazeez fullname: Abdulazeez, Adnan Mohsin
BookMark	eNpNkUtLAzEUhYMoWGs3_oKAO6Ga90yWpWgt1Ac-1iGT3BmmtpOaTCv9905tUVf3cDic78I5Q8dNaAChC0quOdfkBppKUUqYpkeox1TGh4LS7PifPkWDlOaEEJZpIZXsoZcRHoflykbb1hvAr-3ab3Eo8QQaOHgjv4GYbKztAj9C-xXiR8J1gx_A167zpktbAX6OwUFKdVOdo5PSLhIMDreP3u9u38b3w9nTZDoezYaOEUGHlPss97SgqhOWiYwr78CCdoJL55zkhSPcUQ2KkMIxpYqy5ECU96CVFbyPpvteH-zcrGK9tHFrgq3NjxFiZWxsa7cAIxjLcu2FtzkTQtPccVVSRfJcQgfNuq7Lfdcqhs81pNbMwzo23fuGs0xKSrXcEa_2KRdDShHKXyolZjeB-ZuAfwPlkXl3
Cites_doi	10.1007/s44196-025-00920-6 10.1007/s10462-023-10624-y 10.1117/1.JMI.12.S2.S22014 10.1109/ISSC61953.2024.10603268 10.3390/jcm13123556 10.1016/j.imu.2021.100779 10.1109/ACCESS.2024.3370848 10.1109/ACCESS.2025.3555638 10.59247/csol.v3i1.170 10.33899/edusj.2024.148937.1448 10.1016/j.media.2020.101950 10.3390/fi13010008 10.1016/0021-9991(88)90002-2 10.3390/jimaging9030069 10.1016/j.compbiomed.2025.110094 10.1016/j.modpat.2023.100369 10.3390/app15105655 10.3390/brainsci14060559 10.1007/s00530-024-01349-1 10.1007/s43926-025-00130-8 10.1038/s41598-024-84786-2 10.1080/21681163.2025.2476702 10.1038/s41598-025-91416-y 10.1109/ISBI.2018.8363576 10.3390/jimaging11010019 10.1016/j.compbiomed.2024.109248 10.53388/MDM202508014 10.3389/fradi.2023.1336902 10.1016/j.procs.2024.12.037 10.3390/app14156831
ContentType	Journal Article
Copyright	2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU COVID DWQXO HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS DOA
DOI	10.3390/eng6110291
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest Technology Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Korea SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2673-4117
ExternalDocumentID	oai_doaj_org_article_422789d4da8244918c36f160885edce7 10_3390_eng6110291
GroupedDBID	AADQD AAYXX ABDBF ABJCF AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BGLVJ CCPQU CITATION GROUPED_DOAJ HCIFZ IAO IGS ITC M7S MODMG M~E PHGZM PHGZT PIMPY PQGLB PTHSS 8FE 8FG ABUWG AZQEC COVID DWQXO L6V PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c2041-13d78d1b163d7a24736dceae9c435ccc53bc03c19e600bc266bff3e06dde96a43
IEDL.DBID	DOA
ISSN	2673-4117
IngestDate	Mon Dec 01 19:30:35 EST 2025 Wed Nov 26 14:54:11 EST 2025 Wed Nov 05 20:52:34 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c2041-13d78d1b163d7a24736dceae9c435ccc53bc03c19e600bc266bff3e06dde96a43
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4357-7331
OpenAccessLink	https://doaj.org/article/422789d4da8244918c36f160885edce7
PQID	3275511954
PQPubID	5046902
ParticipantIDs	doaj_primary_oai_doaj_org_article_422789d4da8244918c36f160885edce7 proquest_journals_3275511954 crossref_primary_10_3390_eng6110291
PublicationCentury	2000
PublicationDate	20251101
PublicationDateYYYYMMDD	2025-11-01
PublicationDate_xml	– month: 11 year: 2025 text: 20251101 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Eng (Basel, Switzerland)
PublicationYear	2025
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Kavur (ref_38) 2021; 69 ref_14 ref_36 ref_13 ref_35 Nandal (ref_1) 2025; 18 ref_11 ref_10 ref_32 Sindhura (ref_31) 2024; 30 Alajaji (ref_15) 2024; 37 ref_19 ref_18 Chen (ref_2) 2025; 17 ref_16 ref_37 Selvam (ref_20) 2025; 5 Motamed (ref_33) 2021; 27 Islam (ref_9) 2024; 12 Saad (ref_7) 2024; 57 Tariq (ref_21) 2025; 13 Osuala (ref_12) 2025; 12 ref_22 Balasubramaniam (ref_25) 2025; 8 Eker (ref_17) 2024; 58 Ahmed (ref_34) 2013; 4 Osher (ref_24) 1988; 79 ref_3 ref_29 Purwono (ref_26) 2025; 3 ref_28 Kuraning (ref_23) 2025; 252 ref_27 Hussien (ref_30) 2024; 33 ref_8 ref_5 ref_4 ref_6
References_xml	– volume: 18 start-page: 190 year: 2025 ident: ref_1 article-title: Image Denoising Using Quantum Deep Convolutional Generative Adversarial Network for Medical Images publication-title: Int. J. Comput. Intell. Syst. doi: 10.1007/s44196-025-00920-6 – volume: 57 start-page: 19 year: 2024 ident: ref_7 article-title: A survey on training challenges in generative adversarial networks for biomedical image analysis publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-023-10624-y – volume: 12 start-page: S22014 year: 2025 ident: ref_12 article-title: Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks publication-title: J. Med. Imaging doi: 10.1117/1.JMI.12.S2.S22014 – ident: ref_5 doi: 10.1109/ISSC61953.2024.10603268 – ident: ref_32 doi: 10.3390/jcm13123556 – volume: 27 start-page: 100779 year: 2021 ident: ref_33 article-title: Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images publication-title: Inform. Med. Unlocked doi: 10.1016/j.imu.2021.100779 – ident: ref_3 – volume: 12 start-page: 35728 year: 2024 ident: ref_9 article-title: Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3370848 – volume: 13 start-page: 63857 year: 2025 ident: ref_21 article-title: Transforming Brain Tumor Detection Empowering Multi-Class Classification with Vision Transformers and EfficientNetV2 publication-title: IEEE Access doi: 10.1109/ACCESS.2025.3555638 – volume: 3 start-page: 36 year: 2025 ident: ref_26 article-title: Understanding Generative Adversarial Networks (GANs): A Review publication-title: Control Syst. Optim. Lett. doi: 10.59247/csol.v3i1.170 – volume: 33 start-page: 24 year: 2024 ident: ref_30 article-title: Deep Generative Adversarial Networks for Noise Reduction in Medical Images: A Review publication-title: J. Educ. Sci. doi: 10.33899/edusj.2024.148937.1448 – volume: 69 start-page: 101950 year: 2021 ident: ref_38 article-title: CHAOS Challenge—Combined (CT-MR) Healthy Abdominal Organ Segmentation publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101950 – ident: ref_37 – ident: ref_35 doi: 10.3390/fi13010008 – volume: 79 start-page: 12 year: 1988 ident: ref_24 article-title: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations publication-title: J. Comput. Phys. doi: 10.1016/0021-9991(88)90002-2 – ident: ref_14 doi: 10.3390/jimaging9030069 – volume: 17 start-page: e77391 year: 2025 ident: ref_2 article-title: Using the Regression Slope of Training Loss to Optimize Chest X-ray Generation in Deep Convolutional Generative Adversarial Networks publication-title: Cureus – ident: ref_11 doi: 10.1016/j.compbiomed.2025.110094 – volume: 37 start-page: 100369 year: 2024 ident: ref_15 article-title: Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions publication-title: Mod. Pathol. doi: 10.1016/j.modpat.2023.100369 – ident: ref_28 doi: 10.3390/app15105655 – ident: ref_8 doi: 10.3390/brainsci14060559 – volume: 30 start-page: 161 year: 2024 ident: ref_31 article-title: A review of deep learning and Generative Adversarial Networks applications in medical image analysis publication-title: Multimedia Syst. doi: 10.1007/s00530-024-01349-1 – volume: 5 start-page: 39 year: 2025 ident: ref_20 article-title: Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks publication-title: Discov. Internet Things doi: 10.1007/s43926-025-00130-8 – ident: ref_22 doi: 10.1038/s41598-024-84786-2 – ident: ref_19 doi: 10.1080/21681163.2025.2476702 – ident: ref_29 doi: 10.1038/s41598-025-91416-y – ident: ref_13 doi: 10.1109/ISBI.2018.8363576 – ident: ref_18 doi: 10.3390/jimaging11010019 – ident: ref_27 – ident: ref_6 doi: 10.1016/j.compbiomed.2024.109248 – ident: ref_10 – volume: 58 start-page: 101827 year: 2024 ident: ref_17 article-title: BrainPixGAN: Generating intraoperative MRI images with mask-based generative networks publication-title: Eng. Sci. Technol. Int. J. – volume: 8 start-page: 14 year: 2025 ident: ref_25 article-title: Medical Image Enhancement for Improved Diagnostic Accuracy Using Generative Adversarial Network publication-title: Med. Data Min. doi: 10.53388/MDM202508014 – ident: ref_16 doi: 10.3389/fradi.2023.1336902 – volume: 252 start-page: 355 year: 2025 ident: ref_23 article-title: Cycle-Consistent Generative Adversarial Network Based Approach for Denoising CT Scan Images publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2024.12.037 – ident: ref_36 – ident: ref_4 doi: 10.3390/app14156831 – volume: 4 start-page: 1 year: 2013 ident: ref_34 article-title: A New Modified Embedded Zerotree Wavelet Approach for Image Coding (NMEZW) publication-title: Int. J. Sci. Eng. Res.
SSID	ssj0002794565
Score	2.3080113
Snippet	The rapid development of Generative Adversarial Networks (GANs) has transformed medical image processing, enabling realistic image synthesis, augmentation, and...
SourceID	doaj proquest crossref
SourceType	Open Website Aggregation Database Index Database
StartPage	291
SubjectTerms	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
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB509aAH3-L6IqDX4qZJm-Yku6IoyCKi4q2keYgHu7pVwX_vJM26iuDFa1pKyby-TGa-ATgsXOaUMCzpSVslXOcuUbyiSUGpcxValw10TXeXYjgs7u_lVUy4NbGscuITg6M2I-1z5EcsFVkW-MmOn18SPzXK367GERqzMOeZyngH5ganw6vrryxLiuqGkKXlJWV4vj-y9UOOIS-V9EckCoT9v_xxCDJny__9vRVYivCS9Ft9WIUZW6_B4jfSwXW47pOTKeU38YWEH2TkSEtAHdbClOZGed0kw7ZOvCGPNYm3OuTiCb0QiT0G-NENuD07vTk5T-JkhUSnPU4TyowoDK0QjBmhUi5YbrRVVmpET1rrjFW6xzSVFvFQpTGIV84x28vRGcpccbYJnXpU2y0g3PJKGSoLqhgXXEncdKVFphmuI1brwsFkl8vnlkCjxIOHl0U5lUUXBl4AX2940uuwMBo_lNGGSh7adg03qkBQImmhWe5ojn4y87Wsogu7E9mU0RKbciqY7b8f78BC6mf7hj7DXei8jt_sHszr99fHZrwfFesTf5HYmg priority: 102 providerName: ProQuest
Title	A Comparative Study of Generative Adversarial Networks in Medical Image Processing
URI	https://www.proquest.com/docview/3275511954 https://doaj.org/article/422789d4da8244918c36f160885edce7
Volume	6
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2673-4117 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002794565 issn: 2673-4117 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2673-4117 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002794565 issn: 2673-4117 databaseCode: M~E dateStart: 20200101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2673-4117 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002794565 issn: 2673-4117 databaseCode: M7S dateStart: 20210101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2673-4117 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002794565 issn: 2673-4117 databaseCode: BENPR dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2673-4117 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002794565 issn: 2673-4117 databaseCode: PIMPY dateStart: 20210101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PS8MwFA4yPehB_InTOQJ6LVuatGmO29hQ0FLmD-appGkiE-xknYL_vS9p5yYevHh9lLZ8L8n7HnnvewhdRiYwkufU6wqdeUyFxpMsI15EiDEZ7C7t5Joeb3gcR5OJSNZGfdmasEoeuAKuw1yvZs5yGUEkEiRSNDQkhM0R2AJG10cOrGctmXpx12nCUpVKj5RCXt_RxXMIoc4X5EcEckL9v85hF1xGe2i3ZoW4V_3NPtrQxQHaWdMKPETjHh6slLqxrf_7xDODK91oZ3PDlUtplxSOq_LuEk8LXF_G4OtXODxw3RoALz1CD6Ph_eDKqwcieMrvMuIRmvMoJxlwqJxLn3EaAgRSCwWkRykV0Ex1qSJCA43JFMTezBiquyGcYSKUjB6jRjEr9AnCTLNM5kRERFLGmRSAmVQ8UBTsQLGa6GIJUvpW6V6kkC9YKNMVlE3Ut_h9P2G1qp0BPJjWHkz_8mATtZbop_UGKlPq8yBwcnSn__GNM7Tt28G9romwhRqL-bs-R1vqYzEt52202R_Gybjt1lDbln_egS25vk2evgBuQsyY
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VLRJwKFCKurQFS9Bj1Dh24viAUGmpuup2tUKlKqfU8ceqh2bLZgHtn-I3MnaSLqgStx64OpGlZJ5nnu2ZNwDvcpc6JQyLYmnLiOvMRYqXNMopda7E1WWDXNP5UIxG-cWFHK_Ar64WxqdVdj4xOGoz1f6MfI8lIk2DPtmHm2-R7xrlb1e7FhoNLE7s4idu2er3g0O0726SHH06OziO2q4CkU5iTiPKjMgNLZGIGKESLlhmtFVWamQOWuuUlTpmmkqLXKDUGMBK55iNM3QEMlOc4bwPYJUj2PMerI4Hp-Ovt6c6CcIbKVKjg8qYjPdsNckwxCaS_hX5QoOAO_4_BLWjp__b73gGay19JvsN3p_Diq3W4ckfooov4PM-OVhKmhOfKLkgU0cage0wFrpQ18qvPTJq8uBrclWR9taKDK7Ry5K2hgIn3YAv9_JRL6FXTSu7CYRbXipDZU4V44IriUZWWqSa4Thy0T687axa3DQCIQVurLzti6Xt-_DRG_z2DS_qHQams0nR-oiCh7Jkw43KkXRJmmuWOZphHEh9rq7ow3aHhaL1NHWxBMKrfz9-A4-Oz06HxXAwOtmCx4nvYxxqKrehN599tzvwUP-YX9Wz1y2oCVzeN3B-A6F0Nq0
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NT9wwEB1RqKpyKLS06hZoLQHHaOPYieNDhSiw6gq0QnyJW3D8seLQLN1AK_5af13HTsKCKnHj0KsTWUrmeebZnnkDsJm71ClhWBRLW0ZcZy5SvKRRTqlzJa4uG-Sazg_FaJRfXMijOfjT1cL4tMrOJwZHbSban5H3WSLSNOiT9V2bFnG0N9i-_hn5DlL-prVrp9FA5MDe_cbtW_11uIe23kqSwf7p7veo7TAQ6STmNKLMiNzQEkmJESrhgmVGW2WlRhahtU5ZqWOmqbTIC0qNwax0jtk4Q6cgM8UZzvsCFgRHnhDSBk_uz3cSBDqSpUYRlTEZ9201zjDYJpI-ioGhVcA_kSCEt8HS__xjluFNS6rJTrMK3sKcrd7B4gOpxRU43iG7M6Fz4tMn78jEkUZ2O4yF3tS18iuSjJrs-JpcVaS9yyLDH-h7SVtZgZO-h7Nn-agPMF9NKvsRCLe8VIbKnCrGBVcSDa60SDXDcWSoPdjoLFxcN7IhBW63PA6KGQ568M0b__4NL_UdBibTcdF6joKHYmXDjcqRikmaa5Y5mmF0SH0Gr-jBWoeLovU_dTEDxaenH3-BV4iW4nA4OliF14lvbhwKLddg_mZ6a9fhpf51c1VPPwd0E7h8btT8BRi-PfQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Comparative+Study+of+Generative+Adversarial+Networks+in+Medical+Image+Processing&rft.jtitle=Eng+%28Basel%2C+Switzerland%29&rft.au=Abdulqader%2C+Marwa+Mahfodh&rft.au=Abdulazeez%2C+Adnan+Mohsin&rft.date=2025-11-01&rft.issn=2673-4117&rft.eissn=2673-4117&rft.volume=6&rft.issue=11&rft.spage=291&rft_id=info:doi/10.3390%2Feng6110291&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_eng6110291
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2673-4117&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2673-4117&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2673-4117&client=summon