DentDetect for Dental Anomaly Detection

Dental anomaly detection is one of the major challenges in modern dentistry, as correct and timely identification of cavities and fractures helps in proper treatment. Traditional methods of diagnosis may miss subtle abnormalities or be affected by noise in images. This work introduces Dental Detect, an advanced CNN-based system powered by TensorFlow, for automated dental anomaly detection. The proposed system uses cutting-edge CNNs to achieve high accuracy and robustness in the identification of a variety of dental irregularities. It is designed to handle image distortions and noise to ensure reliable performance in different clinical settings. Training on large datasets, Dental Detect provides superior sensitivity and specificity to allow for early diagnosis and intervention. Advantages of the system include higher efficiency in diagnostics, reduced human error, and improved care for patients, which can all lead to better dental health.