Transfer learning has become a significant field of research in radiology. Periapical and panoramic radiographs have long been utilized by dental professionals to assist in the identification of the majority of dental problems. Dental practitioners usually treat tooth decay, also known as caries, physically based on the photographs they obtain from dental labs. To help with the enormous labor load on the healthcare society and for more precision, machine learning aids various smart vision systems based on computer applications. It also has better market demand. In order to provide novel techniques for fully automated tooth caries detection, this study has provided a framework for the recognition and evaluation of dental caries. The methods will make use of categorization and transfer learning techniques. First, using the provided OPG image as input, the suggested system will be able to identify the affected area of the tooth where caries are present. It will be possible to determine whether or not there is caries in the tooth after identifying the area that appears to be diseased. In addition to improving precision and accuracy, this method will also save up the radiologist's time. Dental cavities will be detectable by the X-ray machines, and the findings can be sent directly to the dentist for additional diagnostics.
Dental caries, transfer learning, classification, Convolutional neural network, panoramic images, MI-DCNN (multi-input convolutional neural network), VGG16, ResNet50, MobileNet, Dataset, Comparison, OPG (oorthopantomography)
Trupti Uttamrao Ahirrao , Roshni Bhave "A Novel Approach for Dental Caries Classification Using Transfer Learning" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 312-320
Trupti Uttamrao Ahirrao , Roshni Bhave "A Novel Approach for Dental Caries Classification Using Transfer Learning" Iconic Research And Engineering Journals, 7(4)