Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer

Computers in Biology and Medicine(2022)

Cited 25|Views13
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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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