Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets
Author:
Affiliation:
1. Department of Electrical Engineering, Universitas Indonesia, Depok, Indonesia
2. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia
Funder
Universitas Indonesia through the Hibah Publikasi Terindeks Internasional (PUTI) Kolaborasi Internasional (KI) (2Q2) Scheme
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09850986.pdf?arnumber=9850986
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3. Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
4. Clinical pregnancy is significantly associated with the blastocyst width and area: a time-lapse study
5. A KNN-based classification algorithm for growth stages of Haematococcus pluvialis
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