TY - JOUR
T1 - Generalization and Visual Comprehension of CNN Models on Chromosome Images
AU - Wang, Chengyu
AU - Huang, Daiyun
AU - Su, Jionglong
AU - Yu, Limin
AU - Ma, Fei
N1 - Publisher Copyright:
© 2020 IOP Publishing Ltd. All rights reserved.
PY - 2020/4/8
Y1 - 2020/4/8
N2 - Computer-aided image classification has achieved start-of-the-art performance since Convolutional Neural Network structures were employed. Classical neural networks such as AlexNet and VGG-Net inspired several rules of designing network models. Besides benchmark datasets such as MNIST, CIFAR and ImageNet, classification performance of medical images such as chromosome karyotyping images also improved via Convolutional Neural Network. However, there are few studies on generalization among different datasets. In this paper, we designed a neural network with nine layers, and achieved classification accuracy of 0.984, 0.816 and 0.921 on the dataset of MNIST, CIFAR and chromosome karyotype images. We also visualized the output of several layers of the model and explained that smooth output between neural network layers may induce lower accuracy on classification.
AB - Computer-aided image classification has achieved start-of-the-art performance since Convolutional Neural Network structures were employed. Classical neural networks such as AlexNet and VGG-Net inspired several rules of designing network models. Besides benchmark datasets such as MNIST, CIFAR and ImageNet, classification performance of medical images such as chromosome karyotyping images also improved via Convolutional Neural Network. However, there are few studies on generalization among different datasets. In this paper, we designed a neural network with nine layers, and achieved classification accuracy of 0.984, 0.816 and 0.921 on the dataset of MNIST, CIFAR and chromosome karyotype images. We also visualized the output of several layers of the model and explained that smooth output between neural network layers may induce lower accuracy on classification.
UR - http://www.scopus.com/inward/record.url?scp=85083503519&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1487/1/012027
DO - 10.1088/1742-6596/1487/1/012027
M3 - Conference article
AN - SCOPUS:85083503519
SN - 1742-6588
VL - 1487
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012027
T2 - 2020 4th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2020
Y2 - 17 January 2020 through 19 January 2020
ER -