Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images.

In the medical field, Artificial Intelligence (AI) for diagnostic processes, particularly through deep learning techniques, has become increasingly advanced. Minor trauma, such as accidental cheek biting, sharp dental edges, or poorly fitting dentures, typically causes painful mouth ulcers and bump-like sores inside the mouth. Traditionally, diagnosing these ulcers involves a dentist or physician performing a physical examination, visually assessing the sores, and asking detailed questions about their size, location, duration, and related symptoms. Our research focuses on the advanced classification of oral ulcer stages using a convolutional neural network (CNN). To evaluate performance comprehensively, we developed and tested three custom models, comparing their effectiveness in distinguishing between different stages of oral ulcers. We also explored various optimizers and activation functions to determine the best configuration for improving model performance. Although our models show promising potential as diagnostic tools for oral ulcers, they occasionally make errors. Among the models tested, UlcerNet-2 stood out for its performance. Using the RMSprop optimizer along with Softmax and SELU activation functions, UlcerNet-2 achieved a validation accuracy of 96%. These results highlight UlcerNet-2's exceptional effectiveness in classifying oral ulcer stages, achieving a commendable balance of high accuracy, precision, and recall. This suggests UlcerNet-2 has significant potential as an advanced diagnostic tool, possibly enhancing clinical practices in detecting and staging oral ulcers. The proposed model (UlcerNet) was implemented on FogBus, the cloud framework to empirically evaluate the model performance in cloud-fog interoperable scenarios.