Abstract
Dental disorders can lead to serious implications such as heart attack or strokes if not diagnosed and treated early. The diagnosis of these disorders differs from dentist to dentist due to differences in perception, poor x-ray quality because of noise, and different types of patients. As a result, there is an urgent need to create automated, AI-driven diagnostic solutions for dental disorders. Deep learning solutions have shown outstanding results in automated medical image analysis tasks. Our work proposes a U-Net with attention blocks to segment teeth from dental panoramic X-rays. The proposed Attention U-Net consists of four encoding and decoding blocks and achieved Dice Coefficient, IOU score, Specificity, and F1 Score of 0.9318, 0.8724, 0.9910, and 0.9379, respectively. The Attention U-Net achieves a 1.7% better Dice Coefficient score and 2.9% better IOU (Intersection Over Union) score than one of the best segmentation models, the U-Net. The segmented output can be used in computer-aided diagnostic (CAD) systems to detect various mouth disorders, helping the dentist diagnose the problem efficiently and accurately.
Dishant Padalia
Computer Science Master’s Student at UMass Amherst
My research interests include computer vision, AI in healthcare and natural language processing.