ArticleScientific reports2025
Screening of oral potentially malignant disorders and oral cancer using deep learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.Head and neck pathology · 2026Review
- Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.BMC public health · 2026Article
- Explainable vision transformer framework for multi-class classification and prognostic interpretation of oral cancer in histopathology images.Discover oncology · 2026Article
- Rapid and noninvasive artificial intelligence-assisted diagnostic method for oral squamous cell carcinoma.NPJ digital medicine · 2026Article
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral cancer though preventable, shows high mortality and affect the overall quality of life when detected in late stages. Screening techniques that enable early diagnosis are the need of the hour. The present work aims to evaluate the effectiveness of AI screening tools in the diagnosis of OPMDs and Oral cancers via native or web-application (cloud) using smart phone devices. We trained and tested two deep learning models namely DenseNet201 and FixCaps using 518 images of the oral cavity. While DenseNet201 is a pre-trained model, we modified the FixCaps model from capsule network and trained it ground up. Standardized protocols were used to annotate and classify the lesions (suspicious vs. non-suspicious). In terms of model performance, DenseNet201 achieved an F1 score of 87.50% and AUC of 0.97; while FixCaps exhibited F1 score of 82.8% and AUC of 0.93. DenseNet201 model (20 M) serves as a robust screening model (accuracy 88.6%) that can be hosted on a web-application in the cloud servers; while the adapted FixCaps model with its low parameter size of 0.83 M exhibits comparable accuracy (83.8%) allowing easy transitioning into a native phone-based screening application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.