Medical Image Synthesis using Generative Adversarial Network

Risha, Antara; Islam, Shaira Saiyara; Tahsin, Anika

IUT Repository Home
→
Mechanical and Production Engineering (MPE)
→
Thesis
→
Undergraduate
→
2023
→
View Item

dc.contributor.author	Risha, Antara
dc.contributor.author	Islam, Shaira Saiyara
dc.contributor.author	Tahsin, Anika
dc.date.accessioned	2024-09-06T05:22:02Z
dc.date.available	2024-09-06T05:22:02Z
dc.date.issued	2023-05-30
dc.identifier.citation	[1] Youssef Skandarani, Pierre-Marc Jodoin, and Alain Lalande. Gans for medical image synthesis: An empirical study. Journal of Imaging, 9(3):69, 2023. [2] Meiqin Gong, Siyu Chen, Qingyuan Chen, Yuanqi Zeng, and Yongqing Zhang. Generative adversarial networks in medical image processing. Current Phar maceutical Design, 27(15):1856–1868, 2021. [3] Tonghe Wang, Yang Lei, Yabo Fu, Jacob F Wynne, Walter J Curran, Tian Liu, and Xiaofeng Yang. A review on medical imaging synthesis using deep learning and its clinical applications. Journal of applied clinical medical physics, 22(1):11–36, 2021. [4] Saisai Ding, Jian Zheng, Zhaobang Liu, Yanyan Zheng, Yanmei Chen, Xi aomin Xu, Jia Lu, and Jing Xie. High-resolution dermoscopy image synthesis with conditional generative adversarial networks. Biomedical Signal Process ing and Control, 64:102224, 2021. [5] Siddharth Gupta, Avnish Panwar, Silky Goel, Ankush Mittal, Rahul Ni jhawan, and Amit Kumar Singh. Classification of lesions in retinal fundus images for diabetic retinopathy using transfer learning. In 2019 international conference on information technology (ICIT), pages 342–347. IEEE, 2019. [6] Sandeep B Somvanshi and Nanasaheb D Thorat. Introduction to imaging modalities. In Advances in Image-Guided Cancer Nanomedicine. IOP Pub lishing, 2022. [7] Hamed Alqahtani, Manolya Kavakli-Thorne, and Gulshan Kumar. Applica tions of generative adversarial networks (gans): An updated review. Archives of Computational Methods in Engineering, 28:525–552, 2021. [8] Y Sravani Devi and S Phani Kumar. Dr-dcgan: A deep convolutional gener ative adversarial network (dc-gan) for diabetic retinopathy image synthesis. Webology (ISSN: 1735-188X), 19(2), 2022. 51 [9] Sandra Carrasco Limeros, Sylwia Majchrowska, Mohamad Khir Zoubi, Anna Ros´en, Juulia Suvilehto, Lisa Sj¨oblom, and Magnus Kjellberg. Gan-based gen erative modelling for dermatological applications–comparative study. arXiv preprint arXiv:2208.11702, 2022. [10] Umme Sara, Morium Akter, and Mohammad Shorif Uddin. Image quality assessment through fsim, ssim, mse and psnr—a comparative study. Journal of Computer and Communications, 7(3):8–18, 2019. [11] Rohit Kumar and Vishal Moyal. Visual image quality assessment technique using fsim. International Journal of Computer Applications Technology and Research, 2(3):250–254, 2013. [12] David R Bull. Digital picture formats and representations. Communicating pictures, pages 99–132, 2014.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2166
dc.description	Supervised by Mr. Tareque Mohmud Chowdhury, Assistant Professor, Co-Supervisor Tasnim Ahmed, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Medical Image Synthesis using Generative Adversarial Network	en_US
dc.type	Thesis	en_US