Advancing Oral Health Diagnostics: Superior Disease Classification Using Inception-ResNet-v2 and ResNet18 Deep Learning Models

Oral diseases such as Caries and Ulcers are among the greatest diagnostic challenges facing health care. The article investigates deep learning models for diagnosis improvement, employing ResNet18 and Inception-ResNetv2. The models trained on a wide dataset of oral diseases, and the obtained results demonstrated that Inception-ResNet-v2 outperforms ResNet18. Inception-ResNet-v2 showed exceptional accuracy, especially in classifying Caries, Ulcers, and Tooth Discoloration with near-perfect precision and recall, making it highly reliable for clinical diagnostics. ResNet18 performed well, especially in the balance of accuracy with computational efficiency, though it struggled on more complex conditions like Calculus and Gingivitis. These results show the potential of cutting-edge AI models to disrupt oral health diagnostics, coming up with quicker, more accurate diagnoses that improve patient outcomes. Thus, this study indicates a very promising future in integrating AI with clinical expertise to bring up the standard of care in dental healthcare.