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http://dx.doi.org/10.6109/jkiice.2019.23.11.1357

Performance Comparison of the Optimizers in a Faster R-CNN Model for Object Detection of Metaphase Chromosomes  

Jung, Wonseok (Dept. of Information and Communication Eng., Namseoul University)
Lee, Byeong-Soo (Data mining team, Estmob)
Seo, Jeongwook (Dept. of Information and Communication Eng., Namseoul University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.11, 2019 , pp. 1357-1363 More about this Journal
Abstract
In this paper, we compares the performance of the gredient descent optimizers of the Faster Region-based Convolutional Neural Network (R-CNN) model for the chromosome object detection in digital images composed of human metaphase chromosomes. In faster R-CNN, the gradient descent optimizer is used to minimize the objective function of the region proposal network (RPN) module and the classification score and bounding box regression blocks. The gradient descent optimizer. Through performance comparisons among these four gradient descent optimizers in our experiments, we found that the Adamax optimizer could achieve the mean average precision (mAP) of about 52% when considering faster R-CNN with a base network, VGG16. In case of faster R-CNN with a base network, ResNet50, the Adadelta optimizer could achieve the mAP of about 58%.
Keywords
Faster R-CNN; Gradient descent optimizer; Metaphase chromosome; VGG16; ResNet50;
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