Dental Disease Detection and Classification in Radiograph Images using Deep Learning Model

Dental illnesses are common global health disorders that, if neglected, can have serious consequences for your oral health. Radiographs, which may prove expensive and time-consuming, are a major component of traditional methods for diagnosing and classifying dental diseases. The goal of this study is to suggest a model based on deep learning for dental illness identification and classification sans the use of radiographs, offering an effective and affordable substitute for early detection and care. The Deep Neural Network that has been pre-trained by applying a vast collection of radiograph pictures can be fine-tuned, which is a popular alternative. Radiography picture classification in the database was done using five different levels. The anatomical position, ancillary features, radiodensity, impact on the surrounding framework, and the category of abnormality were all classified here. By making this information available, we hope to improve your capacity to translate radiologist models, automate anomaly identification and categorization in dentistry panoramic radiographs, and progress teeth segmentation algorithms.