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Mobile-based oral cancer classification for point-of-care screening.

  • Author(s): Song, Bofan
  • Sunny, Sumsum
  • Li, Shaobai
  • Gurushanth, Keerthi
  • Mendonca, Pramila
  • Mukhia, Nirza
  • Patrick, Sanjana
  • Gurudath, Shubha
  • Raghavan, Subhashini
  • Imchen, Tsusennaro
  • Leivon, Shirley
  • Kolur, Trupti
  • Shetty, Vivek
  • Bushan, Vidya
  • Ramesh, Rohan
  • Lima, Natzem
  • Pillai, Vijay
  • Wilder-Smith, Petra
  • Sigamani, Alben
  • Suresh, Amritha
  • Kuriakose, Moni
  • Birur, Praveen
  • Liang, Rongguang
  • et al.
Abstract

Significance

Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings.

Aim

To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.

Approach

The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3  MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images.

Results

We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300  ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists.

Conclusions

Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

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