Name
Abstract | ||
|---|---|---|
This paper extends the line of research that considers the application of Artificial Neural Networks (ANNs) as an automated system, for the assignment of tumors grade. One hundred twenty nine cases were classified according to the WHO grading system by experienced pathologists in three classes: Grade I, Grade 11 and Grade III. 36 morphological and textural, cell nuclei features represented each case. These features were used as an input to the ANN classifier, which was trained using a novel stochastic training algorithm, namely, the Adaptive Stochastic On-Line method. The resulting automated classification system achieved classification accuracy of 90%, 94.9% and 97.3% for tumors of Grade 1, II and III respectively. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45224-9_29 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
classification system,neural network,artificial neural network | Stochastic gradient descent,Grading (education),Computer science,Back propagation neural network,Artificial intelligence,Transitional cell bladder carcinoma,Artificial neural network,Classifier (linguistics),Transitional cell carcinoma,Urinary bladder | Conference |
Volume | ISSN | Citations |
2773 | 0302-9743 | 11 |
PageRank | References | Authors |
1.07 | 4 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D.K. Tasoulis | 1 | 490 | 29.51 |
| Panagiota Spyridonos | 2 | 222 | 17.43 |
| Nicos G. Pavlidis | 3 | 78 | 12.48 |
| Dionisis Cavouras | 4 | 224 | 22.08 |
| Panagiota Ravazoula | 5 | 152 | 12.25 |
| George Nikiforidis | 6 | 225 | 21.70 |
| M.N. Vrahatis | 7 | 1740 | 151.65 |