Year: 2025
Type: article
Abstract: OBJECTIVE: This study aims to develop and validate a deep learning-based system for automated identification of oral leukoplakia (OLK), addressing diagnostic challenges in clinical practice. METHODS: We conducted a comparative analysis of 19 convolutional neural network (CNN) architectures using 446 clinical images of histopathologically confirmed oral leukoplakia cases. The dataset was augmented with 1,041 normal oral mucosa images for comparison. A fine-tuned EfficientNetB0 architecture was selected as the optimal model. Class Activation Mapping (CAM) visualized decision-making regions, with performance evaluated through area under the receiver operating characteristic curve (AUC-ROC) analysis and accuracy metrics. RESULTS: The EfficientNetB0 model achieved 97.54% accuracy (95% confidence interval (CI): 95.2%-99.1%) with an AUC of 0.993 (95% CI: 0.981-0.998). Activation mapping demonstrated precise localization of leukoplakic lesions, correlating with clinical diagnostic criteria. The model maintained robust performance across varying illumination conditions and oral cavity locations. CONCLUSION: This deep learning system demonstrates expert-level diagnostic capability for oral leukoplakia identification, showing potential for integration into clinical decision support systems. The model's high diagnostic accuracy and interpretability through activation mapping address critical needs in early oral cancer detection and screening programs.
Source: Cureus
DOI: 10.7759/cureus.83368
Authors: Duo Li, Xiangjian Wang, Jingwen Liu, Jun Sun
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Ningbo University
OpenAlex ID: W4410020739