Dental caries and non caries detection using deep learning

Abstract

Cavities are the most common indication of dental caries, a contagious condition that leads to the deterioration of the tooth's structure. Dental caries has been identified as one of the most common oral health issue. This research has been conducted to identify them early, owing to the discomfort and high expense of treatment. Artificial intelligence has been utilized in recent years to create models which can forecast the risk of dental caries due to restrictions in medical research in oral healthcare, such as the high costs and lengthy requirements. Data for our study were collected from Khyber College of Dentistry and Hospital. On this data, a number of Deep Learning algorithms were implemented, and their performances were evaluated using recall, precision, F1-score, and accuracy. In comparison to CNN, LeNet and AlexNet deep learning techniques, VGG16 has the best performance, scoring accuracy of 98.99%, F1-score of 0.96% with precision of 0.95%, and a recall of 0.97%. This suggested paper demonstrated that DL is strongly advised for dental professionals to use in helping them make decisions for the early diagnosis and treatment of dental caries.