Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning

Bofan Song, Sumsum Sunny, Ross D. Uthoff, Sanjana Patrick, Amritha Suresh, Trupti Kolur, G. Keerthi, Afarin Anbarani, Petra Wilder-Smith, Moni Abraham Kuriakose, Praveen Birur, Jeffrey J Rodriguez, Rongguang Liang

Research output: Contribution to journalArticle

2 Scopus citations


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.

Original languageEnglish (US)
Article number#336298
Pages (from-to)5318-5329
Number of pages12
JournalBiomedical Optics Express
Issue number11
Publication statusPublished - Nov 1 2018


ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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