• Prof. Sujata N Patil
• Dr. Uday Wali
• Dr. M K Swamy

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.

In vitro fertilization, Machine Learning, Convolution Neural Networks, Cloud Services

"DEEP LEARNING TECHNIQUES FOR AUTOMATIC CLASSIFICATION AND ANALYSIS OF HUMAN IN VITRO FERTILIZED (IVF) EMBRYOS. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 2, page no.100-106, February-2018, Available :http://www.jetir.org/papers/JETIR1802014.pdf

"DEEP LEARNING TECHNIQUES FOR AUTOMATIC CLASSIFICATION AND ANALYSIS OF HUMAN IN VITRO FERTILIZED (IVF) EMBRYOS. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 2, page no. pp100-106, February-2018, Available at : http://www.jetir.org/papers/JETIR1802014.pdf