Publication | Closed Access
DEEP LEARNING TECHNIQUES FOR AUTOMATIC CLASSIFICATION AND ANALYSIS OF HUMAN IN VITRO FERTILIZED (IVF) EMBRYOS.
30
Citations
0
References
2018
Year
Image ClassificationConvolutional Neural NetworkImage AnalysisMachine LearningEngineeringPattern RecognitionBioimage AnalysisMedical Image ComputingBiomedical Data ScienceVesselness FiltersConvolution Neural NetworksBiomedical EngineeringDeep LearningComputer VisionImplantation Efficiency
Automated classification of human In Vitro Fertilized (IVF) embryos using Convolution Neural Networks is presented in the paper. Embryos comprise smaller radii cell structures and get differentiated quickly in early days after fertilization, making it difficult to algorithmically track and identify viability of embryo. Machine learning algorithms are yielding better results than alternate methods like Hugh’s circle transforms and modified vesselness filters. The method is useful in increasing the implantation efficiency.