Name
Abstract | ||
|---|---|---|
Lung disease is a growing disease and hence needs lot of attention. It is difficult to delineate the boundary of the lung when it is imaged through X-ray due to poor resolution. Hence, computer aided diagnosis (CAD) is preferred as it assists the radiologists in efficient diagnosis. In this work, a novel supervised classification technique is proposed using histogram of oriented gradient (HOG) and neighborhood preserving embedding (NPE). Our method is evaluated using 2000 chest X-ray images and can efficiently classify normal and abnormal classes with a promising performance of 97.95% accuracy, using support vector machine (SVM) classifier. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/978-1-61499-900-3-1018 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
CAD,X-ray images,HOG,SVM | Biomedical engineering,X-ray,Computer science,Theoretical computer science | Conference |
Volume | ISSN | Citations |
303 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| U. Raghavendra | 1 | 113 | 8.06 |
| Anjan Gudigar | 2 | 52 | 5.72 |
| Tejaswi N. Rao | 3 | 0 | 0.34 |
| Hamido Fujita | 4 | 2644 | 185.03 |
| Rajendra Acharya U | 5 | 4666 | 296.34 |