[논문]중기 염색체 객체 검출을 위한 Faster R-CNN 모델의 최적화기 성능 비교

정원석; 이병수; 서정욱

doi:10.6109/jkiice.2019.23.11.1357

중기 염색체 객체 검출을 위한 Faster R-CNN 모델의 최적화기 성능 비교
Performance Comparison of the Optimizers in a Faster R-CNN Model for Object Detection of Metaphase Chromosomes 원문보기

한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.23 no.11, 2019년, pp.1357 - 1363

정원석 (Dept. of Information and Communication Eng., Namseoul University) , 이병수 (Data mining team, Estmob) , 서정욱 (Dept. of Information and Communication Eng., Namseoul University)

초록
AI-Helper

본 논문은 사람의 중기 염색체로 이루어진 디지털 이미지에서 Faster Region-based Convolutional Neural Network(R-CNN) 모델로 염색체 객체를 검출할 때 필요한 경사 하강 최적화기의 성능을 비교한다. Faster R-CNN의 경사 하강 최적화기는 Region Proposal Network(RPN) 모듈과 분류 점수 및 바운딩 박스 예측 블록의 목적 함수를 최소화하기 위해 사용된다. 실험에서는 이러한 네 가지 경사 하강 최적화기의 성능을 비교하였으며 VGG16이 기본 네트워크인 Faster R-CNN 모델은 Adamax 최적화기가 약 52%의 Mean Average Precision(mAP)를 달성하였고 ResNet50이 기본 네트워크인 Faster R-CNN 모델은 Adadelta 최적화기가 약 58%의 mAP를 달성하였다.

Abstract ▼ AI-Helper

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%.

주제어

표/그림 (9)

그림 Fig. 1 (a) Karyotype Image and (b) Metaphase chromosome image.
그림 Fig. 2 Block diagram of faster R-CNN for object detection of metaphase chromosome images
표 Table. 1 Dataset of Metaphase Chromosomes
그림 Fig. 3 mAP performances of faster R-CNN with VGG16 according to the optimizers
그림 Fig. 4 Loss rate of faster R-CNN with VGG16 (Adamax)
그림 Fig. 5 Precision performance of faster R-CNN with VGG16 (Adamax)
그림 Fig. 6 mAP performances of faster R-CNN with ResNet50 according to the optimizers
그림 Fig. 7 Loss rate of faster R-CNN with ResNet50 (Adadelta)
그림 Fig. 8 Precision performance of faster R-CNN with ResNet50 (Adadelta)

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