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

Yung-Jhe Yan,Nai-Lun Cheng,Mang Ou-Yang,Ming-Hsui Tsai,Jin-Chern Chiou,Chia-Ing Jan

doi:10.3390/s21093219

Abstract

This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.

Highlights

Oral cancer has become a severe health problem in many developing and developed countries
The device uses light-emitting diode (LED) to induce the autofluorescence of target tissue and acquire the spectral images of the autofluorescence; the excitation LED light sources module was equipped with six excitation LEDs; the emission filters on the rotatory filter array passed the spectrum of the autofluorescence within a certain wavelength range of interest and rejected the spectrum without the wavelength range of interest; and the imaging system was composed of a color CMOS imaging sensor and lens capturing the fluorescent image induced from the tissues
A method of the screening of oral cancer was used to observe the autofluorescence of the tissue after the tissue was excited by LEDs

Summary

Introduction

Oral cancer has become a severe health problem in many developing and developed countries. According to the World Health Organization (WHO), 657,000 new cases of oral cancer are diagnosed each year, and more than 330,000 deaths occur due to oral cancer [1]. In Taiwan, oral cancer is ranked as the fifth leading cause of death among common cancers. About 7000 new cases and 3000 deaths of oral cancer occur in Taiwan each year [2]. The incidence rate and mortality rate in Taiwan ranked first and second, respectively, compared with 35 other countries in the OECD. Patients suffering from oral cancer normally have habits of smoking and/or betel-nut chewing in Taiwan and Southeast Asia [3,4,5]

Methods

Results

Conclusion