ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2024
Intelligent deep learning supports biomedical image detection and classification of oral cancer.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.BMC cancer · 2026Pooled it
- Mobile AI-Assisted Oral Lesion Triage for Community Oral Cancer Screening: Prospective Field Evaluation Study.JMIR mHealth and uHealth · 2026Article
- Article
- Enhanced Histopathologic Image Analysis for Mouth Cancer Classification Using Morphological Reconstruction and UNet.Journal of imaging informatics in medicine · 2026Article
- Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.Oral diseases · 2025Article
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
- Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study.Lancet regional health. Americas · 2025Article
- Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.Clinical oral investigations · 2025Article
- Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.Scientific reports · 2025Article
- Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review.Frontiers in oral health · 2025Review
- Review
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3 authors.
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Abstract
backgroundOral cancer is a malignant tumor that usually occurs within the tissues of the mouth. This type of cancer mainly includes tumors in the lining of the mouth, tongue, lips, buccal mucosa and gums. Oral cancer is on the rise globally, especially in some specific risk groups. The early stage of oral cancer is usually asymptomatic, while the late stage may present with ulcers, lumps, bleeding, etc.
objectiveThe objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer.
methodsWe applied two deep learning methods, CNN and Transformers. First, we propose a new CANet classification model for oral cancer, which uses attention mechanisms combined with neglected location information to explore the complex combination of attention mechanisms and deep networks, and fully tap the potential of attention mechanisms. Secondly, we design a classification model based on Swim transform. The image is segmented into a series of two-dimensional image blocks, which are then processed by multiple layers of conversion blocks.
resultsThe proposed classification model was trained and predicted on Kaggle Oral Cancer Images Dataset, and satisfactory results were obtained. The average accuracy, sensitivity, specificity and F1-Socre of Swin transformer architecture are 94.95%, 95.37%, 95.52% and 94.66%, respectively. The average accuracy, sensitivity, specificity and F1-Score of CANet model were 97.00%, 97.82%, 97.82% and 96.61%, respectively.
conclusionsWe studied different deep learning algorithms for oral cancer classification, including convolutional neural networks, converters, etc. Our Attention module in CANet leverages the benefits of channel attention to model the relationships between channels while encoding precise location information that captures the long-term dependencies of the network. The model achieves a high classification effect with an accuracy of 97.00%, which can be used in the automatic recognition and classification of oral cancer.
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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.