A Computational Tool for Automated Detection of Genetic Syndrome Using Facial Images
| Autor: | Carla Diniz Lopes Becker, Thatiane Alves Pianoschi Alva, Eduardo Pooch |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Feature vector Deep learning Feature extraction Pattern recognition 02 engineering and technology computer.software_genre Convolutional neural network Software framework Support vector machine 03 medical and health sciences Kernel (linear algebra) Digital image 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | Intelligent Systems ISBN: 9783030613761 BRACIS |
| DOI: | 10.1007/978-3-030-61377-8_25 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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