A Hybrid Optimization Approach with Deep Learning Technique for the Classification of Dental Caries

Due to the wealth of data available from different radiographic images, detecting dental caries has traditionally been a difficult undertaking. Numerous techniques have been developed to enhance image quality for quicker caries detection. For the investigation of medical images, deep learning has emerged as the preferred methodology. This study provides a thorough examination of the application of deep learning to object detection, segmentation, and classification. It also examines the literature on deep learning-based segmentation and identification techniques for dental images. To identify dental caries, several techniques have been used to date. However, these techniques are inefficient, inaccurate, and unable to handle a sizable amount of datasets. There is a need for a way that can get around these issues since the prior methods failed to do so. In the domains of medicine and radiology, deep convolutional neural networks (CNN) have produced amazing results in predicting and diagnosing diseases. This new field of healthcare research is developing quickly. The current study's objective was to assess the effectiveness of deep CNN algorithms for dental caries detection and diagnosis on radiographic images. The Convolutional Neural Network (CNN) method, which is based on artificial intelligence, is used in this study to introduce hybrid optimal deep learning, which offers superior performance.

@article{Chawla2022,
  title     = {A Hybrid Optimization Approach with Deep Learning Technique for the Classification of Dental Caries},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Riddhi Chawla and Konda Hari Krishna and Araddhana Arvind Deshmukh and K. V. Daya Sagar and Mohammed Saleh Al Ansari and Ahmed I. Taloba},
  doi       = {10.14569/IJACSA.2022.0131241},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131241}
}