Abstract
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.
Highlights
The visual quality of imaging examinations is a major factor impacting the diagnosis process for several oral diseases
We focused on resolution improvement problem) solutions can be categorized based on the tasks they medical imaging, but in order to select the solution to be included in our evaluation, we focus on, i.e., the specific performances classes of images they focus established on
Even considering that in Study 2 we focused on a classification task, using a preprocessing step that improves the spatial resolution of input images is interesting for a
Summary
The visual quality of imaging examinations is a major factor impacting the diagnosis process for several oral diseases. This quality is essential, since accurate identification of anatomical substructures, pathologies, and functional features depends on it. Previous works assessed the visual quality characteristics observed by experts when they evaluated images [1,2]. Along with their overall appearance, experts considered features such as radio density, edge definition, image contrast, and resolution—this last one being mainly related to the sensor’s capacity
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