Diagnosis of dental diseases using curvilinear semantic deep convolutional neural network

Abstract

Dental radiographs are mainly used to diagnose and identify dental newlinedisorders. This thesis discusses few computational techniques in the field of newlinemedical image analysis for detecting and diagnosing of dental diseases such newlineas Periodontal diseases, Enamel caries, Periapical disease, Pericoronal newlinediseases in damaged teeth. In the case of medical images, human interaction newlineand perception are crucial. Identifying fine characteristics and areas of interest newlinefrom various forms of dental radiographs is a tough process. Since a few newlineyears, software engineers and domain specialists have collaborated to newlineestablish a set of scientific tools to assist practitioners (dentists) in newlinedetermining the best treatment based on visual perception, subject knowledge, newlineand computational findings. Radiographic imaging studies in medical practice give greater hints for diagnosis. They are not the ultimate tool since investigations must be correlated with clinical results, as discovered through detailed in-depth newlinediscussions with selected dental specialists. Various dental diseases-related newlineissues are covered in this research work, as well as a proposed method for newlinedetecting dental diseases from dental radiographic image. Four distinct sorts newlineof dental issues, such as Periodontal diseases, Enamel caries, Periapical newlinedisease, and Pericoronal diseases were taken for the analysis. newline newline