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RSS FeedsIJERPH, Vol. 20, Pages 2377: The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases (International Journal of Environmental Research and Public Health)

 
 

29 january 2023 08:02:13

 
IJERPH, Vol. 20, Pages 2377: The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases (International Journal of Environmental Research and Public Health)
 


With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus.


 
83 viewsCategory: Medicine, Pathology, Toxicology
 
IJERPH, Vol. 20, Pages 2371: Understanding Social Exercise: Desire and Intention to Participate in Running Crews (International Journal of Environmental Research and Public Health)
IJERPH, Vol. 20, Pages 2376: Nature-Based Interventions and Exposure among Cancer Survivors: A Scoping Review (International Journal of Environmental Research and Public Health)
 
 
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