Name
Abstract | ||
|---|---|---|
The efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the deep convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets. |
Year | Venue | Keywords |
|---|---|---|
2018 | PROCEEDINGS OF 2018 TENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | dimensionality reduction, deep learning, TensorFlow, JSRT, chest X-ray, segmentation, bone shadow exclusion, t-distributed stochastic neighbor embedding, lung cancer |
Field | DocType | Citations |
Lung cancer,Shadow,Dimensionality reduction,X ray analysis,Pattern recognition,Convolutional neural network,Outlier,Image segmentation,Artificial intelligence,Deep learning,Machine learning,Mathematics | Conference | 1 |
PageRank | References | Authors |
0.35 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Peng | 1 | 7 | 2.54 |
| Wang Zhen | 2 | 1 | 0.35 |
| Wei Zeng | 3 | 118 | 24.27 |
| Yuri G. Gordienko | 4 | 50 | 8.93 |
| Yuriy Kochura | 5 | 11 | 1.58 |
| Oleg Alienin | 6 | 37 | 8.61 |
| Oleksandr Rokovyi | 7 | 4 | 3.18 |
| Sergii Stirenko | 8 | 53 | 14.13 |