Deep Learning for Dental Radiograph Classification: Overcoming Imbalanced Class Challenges in Multi-Condition Diagnosis

It is very important to classify dental radiographs; this helps medical people get diagnosed with some oral health conditions. This paper focuses on the application of convolutional neural networks in dental radiograph classification, where such images are classified accordingly into five categories: Cavity, Fillings, Impacted Tooth, Implant, and Normal. In implementing a deep learning model using TensorFlow and Keras, efficient image classification is conducted. The dataset is augmented using data augmentation for better performance of the model. These modifications help to overcome the challenge in data imbalance and variation in dental images. A lot of care has gone into designing the CNN architecture, and it was trained using the pre-processed dataset. The result from the model evaluation shows the overall performance of the model at 89%. In the precision, recall, and F1-score by category, a very high value for Normal is 0.93, while Implant is 0.86, showcasing practical competency in dental diagnosis. On the contrary, performance in those categories like Cavity is low, showing an F1-score of 0.30, indicating further improvements to be able to identify the subtle categories of dental conditions. This will further provide a details analysis of the classification results by pointing out strengths and limitations for the model. These findings underline the possibility of CNNs to create automated dental image classification, which could enhance diagnostic precision and reduce manual interpretation to assist early detection and treatment planning in dental care.