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Retraction: Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Computer Systems Science and Engineering Editorial Office

Computer Systems Science and Engineering 2024, 48(4), 1083-1083. https://doi.org/10.32604/csse.2024.054265

This article is a retraction of:

Deep Belief Network for Lung Nodule Segmentation and Cancer Detection
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Abstract

This article has no abstract.

The published article titled “Deep Belief Network for Lung Nodule Segmentation and Cancer Detection” has been retracted from Computer Systems Science and Engineering, Vol. 47, No. 1, 2023, pp. 135–151.

DOI: 10.32604/csse.2023.030344

URL: https://www.techscience.com/csse/v47n1/52978

The Editors-in-Chief have retracted this article because, upon further review, in their judgment the article proffers insufficient critical assessment of the material presented and literature cited, which, in their view, invalidates the conclusions drawn.

All authors have not responded to correspondence about this retraction. As a responsible publisher, we hold the reliability and integrity of our published content in high regard. We deeply regret any inconvenience caused by this situation to our readers and all concerned parties.


Cite This Article

APA Style
Computer Systems Science and Engineering Editorial Office (2024). Retraction: deep belief network for lung nodule segmentation and cancer detection. Computer Systems Science and Engineering, 48(4), 1083-1083. https://doi.org/10.32604/csse.2024.054265
Vancouver Style
Computer Systems Science and Engineering Editorial Office. Retraction: deep belief network for lung nodule segmentation and cancer detection. Comput Syst Sci Eng. 2024;48(4):1083-1083 https://doi.org/10.32604/csse.2024.054265
IEEE Style
Computer Systems Science and Engineering Editorial Office,, “Retraction: Deep Belief Network for Lung Nodule Segmentation and Cancer Detection,” Comput. Syst. Sci. Eng., vol. 48, no. 4, pp. 1083-1083, 2024. https://doi.org/10.32604/csse.2024.054265


cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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