Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
| Publication date | 1/12/2003 |
|---|---|
| Host publication | Knowledge-Based Intelligent Information and Engineering Systems: 7th INternational Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part I |
| Publisher | Springer |
| Pages | 199-206 |
| Number of pages | 8 |
| Volume | 2773 PART 1 |
| ISBN (electronic) | 9783540452249 |
| ISBN (print) | 9783540408031 |
| <mark>Original language</mark> | English |
| Event | 7th International Conference, KES 2003 - Oxford, United Kingdom Duration: 3/09/2003 → 5/09/2003 |
| Conference | 7th International Conference, KES 2003 |
|---|---|
| Country/Territory | United Kingdom |
| City | Oxford |
| Period | 3/09/03 → 5/09/03 |
| Name | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
|---|---|
| Publisher | Springer Verlag |
| ISSN (Print) | 0302-9743 |
| Conference | 7th International Conference, KES 2003 |
|---|---|
| Country/Territory | United Kingdom |
| City | Oxford |
| Period | 3/09/03 → 5/09/03 |
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 II 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 I, II and III respectively.