Oral Oncology 156 (2024) 106946

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Oral Oncology
journal homepage: www.elsevier.com/locate/oraloncology

A comprehensive dataset of annotated oral cavity images for diagnosis oforal cancer and oral potentially malignant disorders
N.S. Piyarathne a, b, * , S.N. Liyanage c , R.M.S.G.K. Rasnayaka d , P.V.K.S. Hettiarachchi e, f ,G.A.I. Devindi c , F.B.A.H. Francis c , D.M.D.R. Dissanayake c , R.A.N.S. Ranasinghe c ,M.B.D. Pavithya g , I.B. Nawinne c , R.G. Ragel c , R.D. Jayasinghe faInstitute of Dentistry, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, AB25 2ZR, United Kingdom
Center for Research in Oral Cancer, Department of Basic Sciences, Faculty of Dental Sciences, University of Peradeniya, Kandy, 20400, Sri Lanka
Department of Computer Engineering, Faculty of Engineering, University of Peradeniya, Kandy, 20400, Sri LankadDepartment of Prosthetic Dentistry, Faculty of Dental Sciences, University of Peradeniya, Kandy, 20400, Sri Lankae
Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, 4102, AustraliafDepartment of Oral Medicine and Periodontology, Faculty of Dental Sciences, University of Peradeniya, Kandy, 20400, Sri Lankag
Department of Information Technology, Uppsala University, Uppsala, 75105, Swedenbc

A R T I C L E I N F O

A B S T R A C T

Keywords:
oral cavity images
white light images
oral cancer
oral potentially malignant disorders
machine learning
artificial intellegence

Objectives: This study aims to address the critical gap of unavailability of publicly accessible oral cavity image
datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis andprognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on thehigh prevalence and delayed diagnosis in Asia.
Materials and methods: Following ethical approval and informed written consent, images of the oral cavity wereobtained from mobile phone cameras and clinical data was extracted from hospital records from patients
attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image
categorization and annotations were done by clinicians using a custom-made software tool developed by the
research team.
Results: A dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified
into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped
oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis,
and risk factor profiles such as smoking, alcohol, and betel chewing habits.
Conclusion: Researchers can utilize the annotated images in the COCO format, along with the patients’ metadata,
to enhance ML and AI algorithm development.

Introduction
According to the International Agency for Research on Cancer(IARC), in 2020, there were an estimated 377,713 new cases of lip andoral cavity cancers worldwide, resulting in 177,757 deaths [1]. This
places lip and oral cavity cancers as the 16th most common cancer
globally. Asia reported more than two-thirds of the global oral cancer
incidence [2]. Oral cancer (OCA) is predominantly associated with malesex, advanced age, smoking, alcohol abuse, betel quid chewing habit andlow socioeconomic status [3]. OCA at its early stages demonstrates asimilar presentation to oral potentially malignant disorders (OPMD) andbenign oral mucosal conditions, which makes it diagnosticallychallenging.
Early diagnosis is crucial for successful management; However, in
low- and middle-income countries, facilities are inadequate forscreening and early detection, which contributes to diagnostic delay[1,2,4]. To promote equitable access to healthcare in low-resource set­tings, researchers have focused on developing automated technologies
to aid in the screening and early diagnosis of OCA and OPMD [5].
studies on image-based OCA identification over the last two decades* Corresponding author at: Institute of Dentistry, School of Medicine Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZR, United
Kingdom.
E-mail address: nadisha.piyarathne@abdn.ac.uk (N.S. Piyarathne).
https://doi.org/10.1016/j.oraloncology.2024.106946
Received 25 March 2024; Received in revised form 20 June 2024; Accepted 9 July 2024
Available online 13 July 2024
1368-8375/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

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Oral Oncology 156 (2024) 106946

To address the scarcity of AI ready datasets of oral mucosal diseases,
a comprehensive dataset of annotated oral images is introduced fromthis paper. The annotations and images can be previewed and freely
downloadable in COCO (Common Objects in Context) format [22],widely compatible with various modern ML and AI frameworks.
Material and methods
The development workflow of the WLI dataset is illustrated in
Figure 1 and described in detail in the following subsections.
Ethical approval
This study adhered to ethical standards for research involving humanparticipants specified by the Declaration of Helsinki and its amend­
ments. The ethical approval was obtained from the institutional ethics
review committee at the Faculty of Dental Sciences, University of Per­adeniya, Sri Lanka (ERC/FDS/2022/03). Informed written consent was
obtained from all study participants prior to recruitment, following thestandards for conduct, reporting, editing, and publication.
Data collection
Patients presenting to the Teaching Hospital Peradeniya, Sri Lanka
during 2022–2023 and their bystanders and relatives were recruited forthe study. Individuals with a previous history of any malignancy were
excluded. Clinical oral examination of all participants was carried out by
two collaborating oral medicine specialists. The clinical diagnosis wasrecorded, following which images of all surfaces of the oral cavity wereobtained by dental surgeons in the clinic supervised by the specialists,
using mobile phone cameras under the natural light or the light source ofthe dental chair. Where indicated, histopathological examination was
carried out using incisional biopsy before arriving at a final diagnosis.
Socio-demographic and clinical details of patients were obtained
from hospital records. The details include age at diagnosis, sex, clinicaldiagnosis, histopathological diagnosis (where applicable), and risk fac­
tor profile. Risk factors including smoking, alcohol consumption, andbetel chewing were recorded in binary form, where the habitual users
were marked as yes.
The images that were deemed unsuitable (e.g., unfocused, blurred)
were filtered out, and the data records were arranged in a structured
manner before the dataset was stored in a secure server with convenient
access. All images in the dataset are unaltered photographs with nocomputational processing applied to them. All metadata and images inthe database were anonymized, and no personal information has been
stored.

Fig. 1. Development workflow of the dataset.

relied on histopathological images from publicly available datasets[6,7,8,9]. Some studies utilized oral and dental spectral images [10].
However, obtaining histology and spectral images requires specialized
and costly equipment, making them less suitable for community
settings.
A recent trend involves leveraging machine learning (ML) and arti­
ficial intelligence (AI) technologies for OCA detection using white lightimages (WLI) [11,12]. This approach is especially advantageous in set­tings such as South Asia, where there is a high incidence of OCA and a
low dentist / dental specialist-to-population ratio [13]. However, amajor obstacle impeding research is the lack of publicly accessible WLI
datasets of the oral cavity for training and validation [14]. Some re­searchers have built their own image databases to train algorithms[15,16,17,18]. The development of dataset requires considerable timeand commitment. Thus, publicly available datasets would not only savetime and effort for researchers but also encourage more accurate androbust prediction algorithms.
In 2022, a research study addressing the scarcity of publicly avail­able oral image datasets [14] identified only one existing on kaggle.com[19], this comprised of 131 images of lip and tongue, with 87 labeled ascancerous and 44 as non-cancerous. The limited number of images
within the dataset hinders its suitability for broader, generalizedclassifications.
A Chinese study [18] collected 44,409 clinical oral images using
smartphones for deep learning cancer detection, while another [16]used a smartphone to capture 2350 images of buccal mucosa for an oral
cancer classification model in India. However, these images were notpublished. Additionally, specialized smartphone-based [20] and webbased [21] tools have been introduced for oral image acquisition andannotation. Images gathered using these tools are also not accessible to
the research community.

Image categorization and annotation
The images were classified into four categories, namely oral cancer(OCA), oral potentially malignant disorders (OPMD), benign, andhealthy, by two mid-career dentists supervised by oral medicine spe­cialists, thereby providing a multi-level assessment. A purpose-built
image annotation software tool (OASIS Annotator) was used for image
categorization, to mark polygon-shaped boundaries and labelling.
The OCA category included oral squamous cell carcinomas. OPMD
includes oral submucous fibrosis, leukoplakia, erythroplakia, and oral
lichen planus. Benign lesions include those that did not fall into either of
the above categories, and healthy images were those that did not containany visual changes in the mucosa. While all cases were clinically diag­
nosed by oral medicine specialists, the diagnoses of 100 % of the OCA
category, 56.4 % of the OPMD, and 15.4 % of the Benign category were
histopathology confirmed.
Each image was annotated with boundaries of the oral cavity andlesions where present. The vermillion border was taken as the oral cavity
boundary, and the lesions were marked as close to the visible changes in
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Oral Oncology 156 (2024) 106946

the oral mucosa as possible.
Where multiple lesions of the same category were present, eachlesion was annotated separately. In the presence of multiple lesions ofdifferent categories, only the lesion of the category assigned to the image
was annotated.

Table 1
The number and percentage of images in each category.
OCA

OPMD

BenignHealthy

Image count129
Percentage 4.3 %1394
46.5 %

748
24.9 %

729
24.3 %

Results and discussion
Image data
The dataset comprises of 3000 images derived from n = 714 patients.
Each image is categorized into Healthy, Benign, OPMD, or OCA cate­gories. The number of images in each category is described in Table 1.
The age and sex distributions of the cohort are depicted in Figure 2.
From the total cohort, 59 % were male while 41 % were female. 21 % of
the patients were under 30 years of age, 28 % between 31 and 50 years,
41 % between 51 and 70 years, and 10 % over 71 years.
Image format
The dataset consists of RGB images in the JPG format. Each image isnamed using the format: z_xxx_yy.jpg, where z_xxx represents a uniquepatient ID, and yy denotes the image ID. Landscape-oriented images
have a minimum resolution of 1280 × 597 pixels, while portraitoriented images have a minimum resolution of 654 × 1174 pixels.
Figure 3 shows sample images of each labeled category from the dataset.
Annotation data
Annotations for the images are provided in the JSON (JavaScript
Object Notation) file named Annotations.json. This file contains data
points defining polygon-shaped regions and labels associated with an­
notations, following the COCO format. Annotations can be downloaded
separately, as well as viewed as overlays on images. A sample image
with annotations is shown in Figure 4 with oral cavity and lesion

Fig. 2. Age and sex distribution of the cohort.

Fig. 3. Sample images under each labeled category.
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Oral Oncology 156 (2024) 106946

CRediT authorship contribution statement
N.S. Piyarathne: Writing – original draft, Visualization, Validation,
Methodology, Formal analysis, Conceptualization. S.N. Liyanage:
Writing – original draft, Visualization, Software, Formal analysis, Data
curation. R.M.S.G.K. Rasnayaka: Writing – review & editing, Meth­odology, Formal analysis, Data curation. P.V.K.S. Hettiarachchi:
Writing – review & editing, Supervision, Resources, Methodology. G.A.
I. Devindi: Software, Data curation. F.B.A.H. Francis: Software, Data
curation. D.M.D.R. Dissanayake: Software. R.A.N.S. Ranasinghe:
Software. M.B.D. Pavithya: Methodology, Formal analysis. I.B.
Nawinne: Writing – review & editing, Validation, Supervision, Soft­ware, Resources, Formal analysis, Conceptualization. R.G. Ragel:
Writing – review & editing, Validation, Supervision. R.D. Jayasinghe:
Supervision, Project administration, Funding acquisition, Data curation.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements

Fig. 4. Example image with overlying annotations and labels, oral cavity
boundary (green) and lesion boundary (purple). (For interpretation of the ref­erences to colour in this figure legend, the reader is referred to the web versionof this article.)

The authors thank Mr. Shalindu Malshan, Ms. Yovanthi Jayasinghe,and Ms. K.P.I, Prabhani, for their help in image and data collection. We
acknowledge all study participants with gratitude.

boundaries annotated as overlays along with the respective labels.

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Two CSV (Comma Separated Values) files containing meta-data of
the cohort are included in the dataset, as follows:
1. Imagewise_data.csv: A file containing information about individual
images, including the image ID, category, clinical diagnosis, and thenumber of annotated regions for each image.
2. Patientwise_data.csv: A file containing patient-level metadata,
including age, sex (male/female status as- signed at birth), and total
image count per patient. Additionally, it includes binary risk factors,
including smoking, chewing betel quid, and alcohol consumption.
Conclusions
With its multi-modal data including annotated white light images,
metadata including age, sex, and risk factors, this AI ready dataset
provides a compelling opportunity to create automated tools to aid
diagnosis and prognostication of OCA and OPMD. Representation of awide variety of oral mucosal diseases and single (Sri Lankan) population
are features of concern. AI and ML technologies using WLI have thepotential to deliver improved patient care outcomes through community
screening, early detection, and resolution of socioeconomic inequalities
associated with OCA and OPMD.
Funding
Financial support was provided by the University Research Council,
University of Peradeniya, Sri Lanka, under the Multidisciplinary
Research Grants 2022 (Grant number 127).
Data availability
The dataset described in this study is available in the Zenodo datarepository [23]. It is provided under restricted access and licensed under
CC BY-NC-ND 4.0.
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