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A Computational Tool for Automated Detection of Genetic Syndrome Using Facial Images

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Intelligent Systems (BRACIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12319))

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Early diagnosis of genetic syndromes has a vital importance in the prevention of any potential related health problems. Down syndrome is the most common genetic syndrome. Patients with down syndrome have a high probability of developmental disorders, like Congenital Heart Disease, which is best treated when discovered in the early stages. These patients also have particular facial characteristics that are identified by geneticists in a physical exam. However, there is subjectivity in the professional analysis, which can lead to a late diagnosis, aggravating the patient’s health condition. This paper proposes a software framework for the automatic detection of Down syndrome using facial features extracted from digital images, which could be used as a tool to help in the early detection of genetic syndromes. For training the machine learning model, we create a dataset gathering 170 pictures of children available on the internet. 50% of the pictures were of children with Down syndrome and the other 50% of healthy children. Then, we automatically identify faces and describe the images with facial landmarks. Next, we use two approaches for feature extraction. The first is a traditional computer vision approach using selected distances and angles and textures between the landmarks. The other, a deep learning approach using a Convolutional Neural Network to extract the features automatically. Then, the feature vector is fed to a Support Vector Machine with a linear kernel on both feature extraction approaches. We validate the results measuring the accuracy, sensitivity, and specificity of both feature extraction approaches using 10-fold cross-validation. The deep learning method resulted in an accuracy of 0.94, while the traditional approach achieved 0.84 of accuracy in our dataset. The results shows that the deep learning approach has a higher classification accuracy for this task, even with a small dataset.

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  1. Cerrolaza, J.J., Porras, A.R., Mansoor, A., Zhao, Q., Summar, M., Linguraru, M.G.: Identification of dysmorphic syndromes using landmark-specific local texture descriptors. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (2016)

    Google Scholar 

  2. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.-F.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE, June 2009

    Google Scholar 

  3. Dima, V., Ignat, A., Rusu, C.: Identifying down syndrome cases by combined use of face recognition methods. In: Balas, V.E., Jain, L.C., Balas, M.M. (eds.) SOFA 2016. AISC, vol. 634, pp. 472–482. Springer, Cham (2018).

    Chapter  Google Scholar 

  4. Ekure, E.N., Animashaun, A., Bastos, M., Ezeaka, V.C.: Congenital heart diseases associated with identified syndromes and other extra-cardiac congenital malformations in children in Lagos. West Afr. J. Med. 28(1), 33–37 (2009)

    Google Scholar 

  5. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, New York (2016).

    MATH  Google Scholar 

  6. Hindley, D., Medakkar, S.: Diagnosis of down’s syndrome in neonates. Arch. Dis. Child. Fetal Neonatal Ed. 87(3), F220–1 (2002)

    Article  Google Scholar 

  7. Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  8. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(Jul), 1755–1758 (2009)

    Google Scholar 

  9. Ko, J.M.: Genetic syndromes associated with congenital heart disease. Korean Circ. J. 45(5), 357–361 (2015)

    Article  Google Scholar 

  10. Kruszka, P., et al.: Down syndrome in diverse populations. Am. J. Med. Genet. A 173(1), 42–53 (2017)

    Article  Google Scholar 

  11. Kumov, V., Samorodov, A.: Recognition of genetic diseases based on combined feature extraction from 2D face images. In: 2020 26th Conference of Open Innovations Association (FRUCT), pp. 1–7. IEEE (2020)

    Google Scholar 

  12. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  13. LeCun, Y.: The MNIST database of handwritten digits (1998).

  14. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  15. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  16. LoBue, V., Thrasher, C.: The child affective facial expression (CAFE) set: validity and reliability from untrained adults. Front. Psychol. 5, 1532 (2015)

    Article  Google Scholar 

  17. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  18. Parenting: A special joy: babies with down syndrome galleries, December 2010.

  19. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    Google Scholar 

  20. Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: database and results. Image Vis. Comput. 47, 3–18 (2016)

    Article  Google Scholar 

  21. Sivakumar, S., Larkins, S.: Accuracy of clinical diagnosis in down’s syndrome. Arch. Dis. Child. 89(7), 691 (2004)

    Article  Google Scholar 

  22. Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR abs/1602.07261 (2016).

  23. Zhao, Q., Rosenbaum, K., Okada, K., Zand, D.J., Sze, R., Summar, M., Linguraru, M.G.: Automated down syndrome detection using facial photographs. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3670–3673. IEEE (2013)

    Google Scholar 

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Correspondence to Eduardo Henrique Pais Pooch .

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Pooch, E.H.P., Alva, T.A.P., Becker, C.D.L. (2020). A Computational Tool for Automated Detection of Genetic Syndrome Using Facial Images. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham.

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