Abstract
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.
Highlights
Oral cancer ranks the sixth most common malignant tumor globally with high risk in low- and middle-income countries (LMICs)
After excluding low quality images due to saliva, defocus, motion, low light, and overexposure, 170 image pairs are used in the study and are assigned to categories ‘normal’ or ‘suspicious’, where ‘suspicious’ includes images labeled with oral potentially malignant lesions (OPML) and malignant lesions
We have compared the performance of different networks, applied data augmentation and regularization techniques, and compared our fused dual-modal method with single modal white light imaging (WLI) or autofluorescence imaging (AFI)
Summary
Oral cancer ranks the sixth most common malignant tumor globally with high risk in low- and middle-income countries (LMICs). There are an estimated 529,000 new cases of oral cancer with more than 300,000 deaths each year [1]. The five-year survival rate for oral and oropharyngeal cancers is about 65% in the United States [3] and 37% in India [4]. In India, the five-year survival rate of patients diagnosed at an early stage is 82% whereas the rate is 27% when diagnosed at an advanced stage [5]. With half of all oral cancers worldwide diagnosed at an advanced stage, tools to enable early diagnosis are greatly needed to increase survival rates [6]
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