ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Conference Paper
Publisher : IEEE
Source : 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : The placenta is a complicated organ that plays several roles during fetal evolution. There was a little awareness regarding the connection of anatomical placental disorders with fetal biology. In this particular work, we introduce an open source mathematically traceable deep learning pipeline to examine cell-level placenta histology using neural convolution networks. Also, we have a tendency to learn deep embedded encoding makeup information that's capable of each stratifying 5 distinct cell populations and learn interclass makeup variance. We anticipate that the automation of such a pipeline to population-scale placenta histology research does have the potential to benefit our understanding of the basic cellular placental biology, substantially its role in forecasting adverse birth results. The objective of the present work is to classify the cell populations into 5 classes using the convolution neural network.
Cite this Research Publication : E. Rohith, Sowmya, V., and Dr. Soman K. P., “Convolutional Neural Networks for Placenta Cell Classification”, in 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, 2019.