Abstract
Dental diseases such as caries or gum disease are caused by prolonged exposure to pathogenic plaque. Assessment of such plaque accumulation can be used to identify individuals at risk. In this work we present an automated dental red autofluorescence plaque image classification model based on application of Convolutional Neural Networks (CNN) on Quantitative Light-induced Fluorescence (QLF) images. CNN model outperforms other state of the art classification models providing a 0.75 ± 0.05 F1-score on test dataset. The model directly benefits from multi-channel representation of the images resulting in improved performance when all three colour channels were used.
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References
Bergstra, J., et al.: Theano: deep learning on GPUs with Python. In: NIPS 2011, BigLearning Workshop, Granada, Spain (2011)
Jarrett, K., et al.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153. IEEE (2009)
Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., et al.: Convolutional networks and applications in vision. In: ISCAS, pp. 253–256 (2010)
Lee, E.S., et al.: Association between the cariogenicity of a dental microcosm biofilm and its red fluorescence detected by quantitative light-induced fluorescence-digital (QLF-D). J. Dent. 41(12), 1264–1270 (2013)
Marsh, P.: Microbial ecology of dental plaque and its significance in health and disease. Adv. Dent. Res. 8(2), 263–271 (1994)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pretty, I., et al.: The intra-and inter-examiner reliability of Quantitative Light-induced Fluorescence (QLF) analyses. Br. Dent. J. 193(2), 105–109 (2002)
Stutz, D.: Understanding convolutional neural networks (2014)
Van der Veen, M., et al.: Red autofluorescence of dental plaque bacteria. Caries Res. 40(6), 542–545 (2006)
van der Veen, M.H., Volgenant, C.M., Keijser, B., Jacob Bob, M., Crielaard, W.: Dynamics of red fluorescent dental plaque during experimental gingivitis - a cohort study. J. Dent. 48, 71–76 (2016)
Volgenant, C.M., Mostajo, M.F., Rosema, N.A., Weijden, F.A., Cate, J.M., Veen, M.H.: Comparison of red autofluorescing plaque and disclosed plaque-a cross-sectional study. Clin. Oral Invest. 20, 1–8 (2016)
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Imangaliyev, S., van der Veen, M.H., Volgenant, C.M.C., Keijser, B.J.F., Crielaard, W., Levin, E. (2016). Deep Learning for Classification of Dental Plaque Images. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_34
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DOI: https://doi.org/10.1007/978-3-319-51469-7_34
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