Integrating Open-Source Image Processing with Deep Learning for Enhanced Dental Patient Care in Rural Areas
Keywords:
Dental care, rural areas, image processing, deep learning, periapical radiography, convolutional neural network..Abstract
The objective of this project is to improve dental patient care in remote locations by combining deep learning methods with open-source image processing. A chief radiologist and a team of skilled dentists annotated 196 pairs of periapical radiography pictures that we obtained. The apical areas of teeth were extracted with the application of SIFT and SURF techniques in conjunction with other image preparation procedures. Several image calibration approaches were used in the tooth apical section extraction procedure to enhance feature extraction and matching. For the purpose of classifying dental photos, a convolutional neural network (CNN) was trained using clipped periapical images as inputs. To avoid overfitting, dropout and L2 norm regularization were used. Weight changes were performed using a decreasing learning rate schedule and stochastic gradient descent (SGD). Findings demonstrated higher F1 scores in comparison to baseline techniques, proving the usefulness of the suggested strategy. The work shows how deep learning and image processing might be used to improve dental treatment in remote locations.
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