Objectives To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or potentially malignant using standard real‐time clinical images. Methods A small deep CNN, that uses a pretrained EfficientNet‐B0 as a lightweight transfer learning model, was proposed. A data set of 716 clinical images was used to train and test the proposed model. Accuracy, specificity, sensitivity, receiver operating characteristics (ROC) and area under curve (AUC) were used to evaluate performance. Bootstrapping with 120 repetitions was used to calculate arithmetic means and 95% confidence intervals (CIs). Results The proposed CNN model achieved an accuracy of 85.0% (95% CI: 81.0%–90.0%), a specificity of 84.5% (95% CI: 78.9%–91.5%), a sensitivity of 86.7% (95% CI: 80.4%–93.3%) and an AUC of 0.928 (95% CI: 0.88–0.96). Conclusions Deep CNNs can be an effective method to build low‐budget embedded vision devices with limited computation power and memory capacity for diagnosis of oral cancer. Artificial intelligence (AI) can improve the quality and reach of oral cancer screening and early detection.

Pigmentations of the oral mucosa are common. Gingivae and buccal mucosae are the most common sites for oral pigmentations. Proper history and recognition of clinical features are important for effective management.

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