ArticlePloS one2020
Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Fluorescence Imaging-Assessed Surgical Margin Detection in Head and Neck Oncology by Passive and Active Targeting.Molecular diagnosis & therapy · 2025Review
- Oral Cancer Prediction Using a Probability Neural Network (PNN).Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Identification of Healthy Tissue from Malignant Tissue in Surgical Margin Using Raman Spectroscopy in Oral Cancer Surgeries.Biomedicines · 2023Article
- Autofluorescence Image-Guided Endoscopy in the Management of Upper Aerodigestive Tract Tumors.International journal of environmental research and public health · 2022Review
- Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.Scientific reports · 2022Article
- Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury.Journal of clinical medicine · 2022Article
- Applications of Laser-Induced Fluorescence in Medicine.Sensors (Basel, Switzerland) · 2022Article
- Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy.Journal of personalized medicine · 2021Article
- Article
- Correlation between Autofluorescence Intensity and Histopathological Features in Non-Melanoma Skin Cancer: An Ex Vivo Study.Cancers · 2021Article
- Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection.Sensors (Basel, Switzerland) · 2021Article
- Article
- Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection.Cancers · 2020Article
- Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral 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
methodsFive 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).
resultsThe 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.
conclusionThe QDA algorithm outperforms the LDA classifier in the analysis of autofluorescence images with respect to all of the standard evaluation parameters.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.