Paper Title : Bayesian Polytrees with Learned Deep Features for Multi-Class Cell Segmentation

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i2p13

Authors: R.Navin Kumar MCA., M.Phil. , Sarath Kumar P

         



MLA Style: R.Navin Kumar MCA., M.Phil. , Sarath Kumar P "Bayesian Polytrees with Learned Deep Features for Multi-Class Cell Segmentation " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: R.Navin Kumar MCA., M.Phil. , Sarath Kumar P " Bayesian Polytrees with Learned Deep Features for Multi-Class Cell Segmentation " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
This project studies the application of machine learning in the analysis and diagnosis of Multi-class cell segmentation. The algorithm is evaluated on simulated data and on two publicly available fluorescence microscopy datasets, outperforming directed trees and three state-of-the-art convolutional neural networks, namely SegNet, DeepLab and PSPNet. And then, four machine learning algorithms including Bayesian Polytree, linear regression, support vector machine and logistic regression have been employed to the data sets. The performance comparisons of accuracy and recall rate among different algorithms indicate that the Bayesian Polytree algorithm has the optimal performance over the other two in both data sets. oreover, the comparisons have been carried out in the cases with and without deviation standardization for each algorithm, and the results demonstrate that the deviation standardization has a certain effect on the accuracy improvement.

Reference
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Keywords
—— Cell Segmentation, Microscopy, Bayesian polytree, SegNet, Machine Learning, Neural Networks.