Experimental Analysis of an Efficient Dental Carries Prediction System Based on Improved Convolutional Neural Network (iCNN) Principle

Dental caries is a kind of infectious illness that weakens the structure of teeth, leading to cavities. Because of the discomfort and expense of treatment, studies on dental caries have focused on early diagnosis, as it is one of the most common oral health problems. Due to the high costs and lengthy research times associated with oral healthcare, artificial intelligence has recently been employed to create models that can forecast the likelihood of dental caries. The agony and expensive price of therapy have prompted this investigation toward early detection. Models that may forecast the possibility of dental caries have recently been developed using artificial intelligence. This is in response to the high expenditures and significant time commitments connected with medical research in oral healthcare. In this study, a new method for efficiently identifying dental caries is presented using an Improved Convolutional Neural Network (iCNN). To test how well the suggested scheme works, it is cross-validated with the standard method called Support Vector Machine (SVM). This paper's suggested method, iCNN, demonstrates that dental practitioners may greatly benefit from deep learning to aid in the early diagnosis and treatment of dental caries.