Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis

Abstract

Background Oral cancer is one of the most common diseases globally. Conventional oral examination and histopathological examination are the two main clinical methods for diagnosing oral cancer early. VELscope is an oral cancer-screening device that exploited autofluorescence. It yields inconsistent results when used to differentiate between normal, premalignant and malignant lesions. We develop a new method to increase the accuracy of differentiation. Materials and methods Five samples (images) of each of 21 normal mucosae, as well as 31 premalignant and 16 malignant lesions of the tongue and buccal mucosa were collected under both white light and autofluorescence (VELscope, 400-460 nm wavelength). The images were developed using an iPod (Apple, Atlanta Georgia, USA). Results The normalized intensity and standard deviation of intensity were calculated to classify image pixels from the region of interest (ROI). Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers were used. The performance of both of the classifiers was evaluated with respect to accuracy, precision, and recall. These parameters were used for multiclass classification. The accuracy rate of LDA with un-normalized data was increased by 2% and 14% and that of QDA was increased by 16% and 25% for the tongue and buccal mucosa, respectively. Conclusion The QDA algorithm outperforms the LDA classifier in the analysis of autofluorescence images with respect to all of the standard evaluation parameters.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy

“…One limitation of this study was the use of the same data for both training and validation. Jeng et al performed autofluorescence imaging using the VELscope instrument (LED Dental, Vancouver-Canada) in healthy volunteers ( n = 22) and patients presenting either premalignant ( n = 31) or malignant ( n = 16) oral lesions [ 39 ]. The data were divided into training and testing sets, the average and standard deviation of the autofluorescence intensity within ROIs were computed as features, and LDA and QDA models were trained for the discrimination among cancerous, precancerous, and healthy oral tissues.…”

Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection

“…Recently, Jeng et al [ 32 ] developed a principle component analysis based method that combined a VELscope and Raman spectroscopy to improve the detection of oral cancer. Jeng et al [ 32 ] further used linear discriminant analysis and quadratic discriminant analysis to increase differentiation between normal, premalignant, and malignant lesions based on the autofluorescence images acquired from the VELscope; the accuracy of the classifications was increased by 2% to 14% [ 33 ]. Huang et al [ 34 ] created a two-channel autofluorescence detection that used 375 and 460 nm excitation light sources and 479 and 525 nm band-pass emission filters to detect oral cancer and precancerous lesions.…”

A Cytological Study Enlightening the Unseen Effects of Concomitant Chemoradiotherapy in Contralateral Normal Buccal Mucosa of Oral Squamous Cell Carcinoma Patients

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