A Hybrid Attention U-Net Model for Enhanced Radiographic Detection of Dental Caries | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Tilottama Pankaj Dhake: Department, K J Somaiya Institute of Technology,Mumbai,India

2. Namrata F Ansari: Department, K J Somaiya Institute of Technology,Mumbai,India

Abstract:

Dental caries detection is crucial for effective treatment and prevention. This study presents a novel Hybrid Attention U-Net (HAU-Net) architecture, specifically designed to improve dental caries detection in panoramic X-ray images. Leveraging advanced attention mechanisms, dilation convolutions, batch normalization, and dropout layers, HAU-Net enhances feature extraction and segmentation accuracy. Evaluations on a dataset of 100 images from Narkhede Dental Clinic in Thane demonstrate significant improvements in detection capabilities, achieving superior accuracy, loss, and segmentation quality. HAU-Net shows significant potential for enhancing dental care through automated diagnostics, early lesion detection, and informed treatment planning.

Page(s): 834-844

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 4, Year: 2024

Keywords:

deep learning , image segmentation , Dental caries , radiographic images , dental diagnostics , UNet , automated detection

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