Detection of Primary and Secondary Cancers Using Raman Spectroscopy and Self-Constructing Neural Networks

Dehghani-Bidgoli, Z. and Khamechian, T. (2019) Detection of Primary and Secondary Cancers Using Raman Spectroscopy and Self-Constructing Neural Networks. Journal of Applied Spectroscopy, 86 (3). pp. 528-532.

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Abstract

The present study is aimed to propose a new method for the optimization of neural networks, known as self-constructing neural network (SCNN), to discriminate the Raman spectra of normal tissues, as well as primary and metastatic (secondary) cancers. According to the results, this novel method could significantly improve the ability of the neural network and thoroughly classify the Raman spectra relating to the pathologic states (100 accuracy). © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Item Type: Article
Additional Information: cited By 0
Subjects: Pathology
Divisions: Faculty of Medicine > Basic Sciences > Department of Pathology& Histology
Depositing User: ART . editor
Date Deposited: 31 Dec 2019 11:00
Last Modified: 31 Dec 2019 11:00
URI: http://eprints.kaums.ac.ir/id/eprint/4504

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