Deep Learning for Dental Caries Classification: Leveraging Sequential Models for Accurate Diagnosis

Dental caries, a widespread oral health concern, may necessitate the extraction of affected teeth. However, the application of deep learning techniques significantly improves the accuracy of dental caries classification. Employing sequential models, this study utilizes TensorFlow and Keras to construct a Convolutional Neural Network (CNN). Through training and evaluation on dental image datasets, the effectiveness of the model is showcased via metrics such as accuracy, a confusion matrix, and a comprehensive classification report. Furthermore, the study includes the generation of a Receiver Operating Characteristic (ROC) curve accompanied by Area Under the Curve (AUC) analysis and a heatmap visualization. This research presents a robust tool for precise dental caries diagnosis, thus contributing to enhanced healthcare outcomes.