Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach-Reference-Cited by-同舟云学术

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Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach

Published:2023-10-03 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Prasad S. V. S,Rao B. Chinna,Rao M. Koteswara,Kumar K. Ravi,Vara Prasad Srisailapu D.,Ramesh Chappa

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-16980-9.pdf

Reference29 articles.

1. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281–2292

2. Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32:582–596

3. Vardhana M, Arunkumar N, Lasrado S, Abdulhay E, Ramirez-Gonzalez G (2018) Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cogn Syst Res 50:10–14

4. Minnema J, van Eijnatten M, Kouw W, Diblen F, Mendrik A, Wolff J (2018) CT image segmentation of bone for medical additive manufacturing using a convolutional neural network. Comput Biol Med 103:130–139

5. Nguyen TP, Choi S, Park SJ, Park SH, Yoon J (2021) Inspecting method for defective casting products with convolutional neural network (CNN). Intl J Precision Eng Manuf Green Technol 8:583–594

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