Name
Title | ||
|---|---|---|
Comparative evaluation of support vector machines and probabilistic neural networks in superficial bladder cancer classification |
Abstract | ||
|---|---|---|
In this study a comparative evaluation of Support Vector Machines (SVMs) and Probabilistic neural networks (PNNs) was performed exploring their ability to classify superficial bladder carcinomas as low or high-risk. Both classification models resulted in a relatively high overall accuracy of 85.3% and 83.7% respectively. Descriptors of nuclear size and chromatin cluster patterns were participated in both best feature vectors that optimized classification performance of the two classifiers. Concluding, the good performance and consistency of the SVM and PNN models enforces the belief that certain nuclear features carry significant diagnostic information and renders these techniques viable alternatives in the diagnostic process of assigning urinary bladder carcinomas grade. |
Year | Venue | Keywords |
|---|---|---|
2006 | J. Comput. Meth. in Science and Engineering | best feature vector,support vector machine,probabilistic neural network,significant diagnostic information,classification model,best feature combination,certain nuclear feature,nuclear size,comparative evaluation,classification scheme,nuclear feature,superficial bladder carcinoma,feature space,diagnostic process,different classification,superficial urinary bladder,urinary bladder carcinomas grade,pnn model,good performance,optimized classification performance,superficial bladder cancer classification,support vector machines,image analysis |
Field | DocType | Volume |
Feature vector,Brute-force search,Pattern recognition,Computer science,Support vector machine,Bladder cancer,Artificial intelligence,Probabilistic logic,Artificial neural network,Urinary bladder,Urinary Bladder Tumors,Machine learning | Journal | 6 |
Issue | ISBN | Citations |
5 | 981-238-595-9 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panagiota Spyridonos | 1 | 222 | 17.43 |
| P. Petalas | 2 | 0 | 0.34 |
| Dimitris Glotsos | 3 | 139 | 12.43 |
| George Nikiforidis | 4 | 16 | 2.77 |
| Dionisis Cavouras | 5 | 21 | 4.67 |
| Panagiota Ravazoula | 6 | 152 | 12.25 |