Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer
Computers in Biology and Medicine(2022)
Abstract
The early detection of laryngeal cancer significantly increases the survival rates, permits more conservative larynx sparing treatments, and reduces healthcare costs. A non-invasive optical form of biopsy for laryngeal carcinoma can increase the early detection rate, allow for more accurate monitoring of its recurrence, and improve intraoperative margin control.
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Key words
Raman spectroscopy,Laryngeal cancer,Random forest,Convolutional neural network,Principal component analysis
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