DETECTION OF PRIMARY AND SECONDARY CANCERS USING RAMAN SPECTROSCOPY AND SELF-CONSTRUCTING NEURAL NETWORKS
Abstract
The present study 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).
About the Authors
Zohreh Dehghani-Bidgoli
Islamic Azad University, Kashan Branch
Islamic Republic of Iran
Islamic Republic of Iran
Department of Biomedical Engineering
Kashan
Tahereh Khamechian
Kashan University of Medical Sciences
Islamic Republic of Iran
Islamic Republic of Iran
Department of Medical Pathology
Kashan
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Review
For citations:
Dehghani-Bidgoli Z., Khamechian T. DETECTION OF PRIMARY AND SECONDARY CANCERS USING RAMAN SPECTROSCOPY AND SELF-CONSTRUCTING NEURAL NETWORKS. Zhurnal Prikladnoii Spektroskopii. 2019;86(3):489(1)-489(5).
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