Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?
Sensors 2021, 21(6), 2013; https://doi.org/10.3390/s21062013 (registering DOI)
Received: 4 January 2021 / Revised: 5 March 2021 / Accepted: 9 March 2021 / Published: 12 March 2021
(This article belongs to the Special Issue Sensing and Imaging Technology in Dentistry)
Resolution plays an essential role in oral imaging for periodontal
disease assessment. Nevertheless, due to limitations in acquisition
tools, a considerable number of oral examinations have low resolution,
making the evaluation of this kind of lesion difficult. Recently, the
use of deep-learning methods for image resolution improvement has seen
an increase in the literature. In this work, we performed two studies to
evaluate the effects of using different resolution improvement methods
(nearest, bilinear, bicubic, Lanczos, SRCNN, and SRGAN). In the first
one, specialized dentists visually analyzed the quality of images
treated with these techniques. In the second study, we used those
methods as different pre-processing steps for inputs of convolutional
neural network (CNN) classifiers (Inception and ResNet) and evaluated
whether this process leads to better results. The deep-learning methods
lead to a substantial improvement in the visual quality of images but do
not necessarily promote better classifier performance.
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