Dental caries is a microbial disease that results in localized dissolution of the mineral content of dental tissue. Despite
considerable decline in the incidence of dental caries, it remains a major health problem in many societies. Early
detection of incipient lesions at initial stages of demineralization can result in the implementation of non-surgical
preventive approaches to reverse the demineralization process. In this paper, we present a novel approach combining
deep convolutional neural networks (CNN) and optical coherence tomography (OCT) imaging modality for
classification of human oral tissues to detect early dental caries. OCT images of oral tissues with various densities were
input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN
automatically learns a hierarchy of increasingly complex features and a related classifier directly from training data sets.
The initial CNN layer parameters were randomly selected. The training set is split into minibatches, with 10 OCT
images per batch. Given a batch of training patches, the CNN employs two convolutional and pooling layers to extract
features and then classify each patch based on the probabilities from the SoftMax classification layer (output-layer).
Afterward, the CNN calculates the error between the classification result and the reference label, and then utilizes the
backpropagation process to fine-tune all the layer parameters to minimize this error using batch gradient descent
algorithm. We validated our proposed technique on ex-vivo OCT images of human oral tissues (enamel, cortical-bone,
trabecular-bone, muscular-tissue, and fatty-tissue), which attested to effectiveness of our proposed method.
Nima Karimian, Hassan S. Salehi, Mina Mahdian, Hisham Alnajjar, and Aditya Tadinada, "Deep learning classifier with optical coherence tomography images for early dental caries detection," Proc. SPIE 10473, Lasers in Dentistry XXIV, 1047304 (Presented at SPIE BiOS: January 28, 2018; Published: 8 February 2018); https://doi.org/10.1117/12.2291088.
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