Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning
| Autor: | Jeffrey J. Rodriguez, G. Keerthi, Bofan Song, Sanjana Patrick, Praveen Birur, Sumsum P. Sunny, Petra Wilder-Smith, Afarin Anbarani, Trupti Kolur, Moni Abraham Kuriakose, Amritha Suresh, Rongguang Liang, Ross D. Uthoff |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Contextual image classification
Artificial neural network business.industry Computer science Deep learning Hyperspectral imaging Pattern recognition Image processing Optical Physics Materials Engineering 01 natural sciences Convolutional neural network Atomic and Molecular Physics and Optics Article 010309 optics 03 medical and health sciences 0302 clinical medicine ComputingMethodologies_PATTERNRECOGNITION 030220 oncology & carcinogenesis 0103 physical sciences Medical imaging Artificial intelligence business Transfer of learning Biotechnology |
| Zdroj: | Biomedical optics express, vol 9, iss 11 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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