Oral cancer is a major health problem in the world, with significant morbidity and mortality rates that are mostly because they are not diagnosed early enough or because the lesions are often not detected at an early stage. Intraoral imaging has become a convenient and non-invasive screening tool, but diagnostic performance is highly reliant on clinician experience, and there is a wide range of heterogeneous imaging conditions across devices and clinical environments. This paper will attempt to overcome these shortcomings by introducing OralLesionNet, a dense multi-scale convolutional neural network (CNN) architecture that can effectively detect oral cancer using intraoral images with a higher degree of domain generalisation. The proposed model incorporates a dense multi-scale backbone to achieve fine-grained textural features, as well as coarse morphological features, to facilitate a better ability to differentiate benign and malignant lesions. The domain generalisation module is included in the training to promote the learning of invariant features to mitigate the performance decline due to inter-centre and device-related variations. The model was tested on a free, publicly available intraoral image dataset of benign and malignant cases that were clinically verified. To obtain a credible assessment, extensive preprocessing, augmentation, and train-validation-test splitting were used. The experimental outcomes indicate that OralLesionNet is more accurate, more precise, more recalls, more F1-score, and more area under the ROC curve (AUC) than baseline CNN, ResNet and DenseNet architectures. Ablation experiments validate the claims that multi-scale design and domain generalisation strategy play an important role in enhancing performance. The sensitivity obtained with the proposed framework is very high; this is very important in screening applications where false negatives should be reduced to a minimum. These results suggest that oral lesion classification can be made more robust and generalizable with dense multi-scale representation and domain-aware learning. OralLesionNet has a good potential of becoming a computer-aided diagnostic support tool that could enhance the ability to detect at earlier stages, minimise the inter-observer variability, and expand the screening to resource-limited settings.