1. The establishment of a multi-source dataset of images on common oral lesions
- Author
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Jian Han Lim, Wanninayake Mudiyanselage Tilakaratne, Jyotsna Rimal, Ruwan Duminda Jayasinghe, Chee Sun Liew, Yi-Hsin Yang, Rahmi Amtha, R.A. Welikala, Chee Seng Chan, Ying Zhi Lim, Rosnah Binti Zain, Senthilmani Rajendran, Thomas George Kallarakkal, Alexander Ross Kerr, John Gibson, Karthikeya Patil, Sok Ching Cheong, Kohgulakuhan Yogalingam, Sarah Barman, and Paolo Remagnino
- Subjects
Text mining ,Computer science ,business.industry ,Data mining ,business ,computer.software_genre ,computer ,Multi-source - Abstract
Purpose To establish an oral lesion image database that could accelerate the development of artificial intelligence systems for lesion recognition and referral decision. Materials and Methods We describe the establishment of a multi-sourced image dataset through the development of a platform for the collection and annotation of images. Further, we developed a used-friendly tool (MeMoSA® ANNOTATE) for systematic annotation to collect a rich dataset associated with the images. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions to identify lesions that are challenging to identify through images alone. Results The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders, benign lesions, normal anatomical variants and normal mucosa that were collected through our platform, MeMoSA® UPLOAD. Over 800 images were annotated by seven oral medicine specialists on MeMoSA®ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3–100%). Conclusion This is the first description of a database with well-annotated oral lesions. This database has already been used for the development of AI algorithm for classifying oral lesions. Further expansion of this database could accelerate the improvement in AI algorithms that can facilitate the early detection of oral potentially malignant disorders and oral cancer.
- Published
- 2021