Name
Abstract | ||
|---|---|---|
Deep learning architectures particularly Convolutional Neural Network (CNN) have shown an intrinsic ability to automatically extract the high level representations from big data. CNN has produced impressive results in natural image classification, but there is a major hurdle to their deployment in medical domain because of the relatively lack of training data as compared to general imaging benchmarks such as ImageNet. In this paper we present a comparative evaluation of the three milestone architectures i.e. LeNet, AlexNet and GoogLeNet and propose our CNN architecture for classifying medical anatomy images. Based on the experiments, it is shown that the proposed Convolutional Neural Network architecture outperforms the three milestone architectures in classifying medical images of anatomy object. |
Year | Venue | Field |
|---|---|---|
2017 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Architecture,Anatomy,Software deployment,Convolutional neural network,Visualization,Convolution,Computer science,Artificial intelligence,Deep learning,Contextual image classification,Big data |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sameer Khan | 1 | 1 | 0.70 |
| Suet-Peng Yong | 2 | 30 | 5.94 |