Welikala, Roshan Alex and Remagnino, Paolo and Lim, Jian Han and Chan, Chee Seng and Rajendran, Senthilmani and Kallarakkal, Thomas George and Zain, Rosnah Binti and Jayasinghe, Ruwan Duminda and Rimal, Jyotsna and Kerr, Alexander Ross and Amtha, Rahmi and Patil, Karthikeya and Tilakaratne, Wanninayake Mudiyanselage and Gibson, John and Cheong, Sok Ching and Barman, Sarah Ann (2020) Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access, 8. pp. 132677-132693. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2020.3010180.
Full text not available from this repository.Abstract
Oral cancer is a major global health issue accounting for 177,384 deaths in 2018 and it is most prevalent in low- and middle-income countries. Enabling automation in the identification of potentially malignant and malignant lesions in the oral cavity would potentially lead to low-cost and early diagnosis of the disease. Building a large library of well-annotated oral lesions is key. As part of the MeMoSA(R)(Mobile Mouth Screening Anywhere) project, images are currently in the process of being gathered from clinical experts from across the world, who have been provided with an annotation tool to produce rich labels. A novel strategy to combine bounding box annotations from multiple clinicians is provided in this paper. Further to this, deep neural networks were used to build automated systems, in which complex patterns were derived for tackling this difficult task. Using the initial data gathered in this study, two deep learning based computer vision approaches were assessed for the automated detection and classification of oral lesions for the early detection of oral cancer, these were image classification with ResNet-101 and object detection with the Faster R-CNN. Image classification achieved an F-1 score of 87.07% for identification of images that contained lesions and 78.30% for the identification of images that required referral. Object detection achieved an F-1 score of 41.18% for the detection of lesions that required referral. Further performances are reported with respect to classifying according to the type of referral decision. Our initial results demonstrate deep learning has the potential to tackle this challenging task.
| Item Type: | Article |
|---|---|
| Funders: | UK Research & Innovation (UKRI) Medical Research Council UK (MRC) (MR/S013865/1) |
| Uncontrolled Keywords: | Cancer; Lesions; Image classification; Machine learning; Cavity resonators; Image segmentation; Composite annotation; Deep learning; Image classification; Object detection; Oral cancer; Oral potentially malignant disorders |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RK Dentistry |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Dentistry > Dept of Oral & Maxillofacial Surgery |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 15 Mar 2023 04:07 |
| Last Modified: | 15 Mar 2023 04:07 |
| URI: | http://eprints.um.edu.my/id/eprint/37201 |
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