Open AccessAutomatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning
Author(s) -
Bofan Song,
Sumsum Sunny,
Ross D. Uthoff,
Sanjana Patrick,
Amritha Suresh,
Trupti Kolur,
G. Keerthi,
Afarin Anbarani,
Petra WilderSmith,
Moni Abraham Kuriakose,
Praveen Birur,
Jeffrey J. Rodríguez,
Rongguang Liang
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.005318
Subject(s) - deep learning , artificial intelligence , computer science , convolutional neural network , transfer of learning , autofluorescence , contextual image classification , image processing , pattern recognition (psychology) , computer vision , machine learning , image (mathematics) , optics , physics , fluorescence
With the goal to screen high-risk populations for oral cancer in low- and middle-income countries (LMICs), we have developed a low-cost, portable, easy to use smartphone-based intraoral dual-modality imaging platform. In this paper we present an image classification approach based on autofluorescence and white light images using deep learning methods. The information from the autofluorescence and white light image pair is extracted, calculated, and fused to feed the deep learning neural networks. We have investigated and compared the performance of different convolutional neural networks, transfer learning, and several regularization techniques for oral cancer classification. Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection.