Name
Abstract | ||
|---|---|---|
In this work, four well known convolutional neural networks (CNNs) that were pretrained on the ImageNet database are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. The images are classified using three different transfer learning strategies and a experimental setup specifically adapted for the classification of endoscopic imagery. The CNNs are either used as fixed feature extractors without any fine-tuning to our endoscopic celiac disease image database or they are fine-tuned by training either all layers of the CNN or by fine-tuning only the fully connected layers. Classification is performed by the CNN SoftMax classifier as well as linear support vector machines. The CNN results are compared with the results of four state-of-the-art image representations. We will show that fine-tuning all the layers of the nets achieves the best results and outperforms the comparison approaches. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IPTA.2016.7821020 | 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) |
Keywords | Field | DocType |
Convolutional neural networks,Transfer learning,Celiac Disease,Automated diagnois,Endoscopy | Computer vision,Pattern recognition,Softmax function,Convolutional neural network,Computer science,Support vector machine,Image representation,Transfer of learning,Feature extraction,Artificial intelligence,Image database,Classifier (linguistics) | Conference |
ISSN | ISBN | Citations |
2154-512X | 978-1-4673-8911-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georg Wimmer | 1 | 19 | 4.06 |
| Andreas Vécsei | 2 | 167 | 18.36 |
| Andreas Uhl | 3 | 1958 | 223.07 |