Standard
Urinary bladder tumor grade diagnosis using on-line trained neural networks. / Tasoulis, D. K.; Spyridonos, P.
; Pavlidis, N. G. et al.
Knowledge-Based Intelligent Information and Engineering Systems: 7th INternational Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part I. Vol. 2773 PART 1 Springer, 2003. p. 199-206 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Tasoulis, DK, Spyridonos, P
, Pavlidis, NG, Cavouras, D, Ravazoula, P, Nikiforidis, G & Vrahatis, MN 2003,
Urinary bladder tumor grade diagnosis using on-line trained neural networks. in
Knowledge-Based Intelligent Information and Engineering Systems: 7th INternational Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part I. vol. 2773 PART 1, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Springer, pp. 199-206, 7th International Conference, KES 2003, Oxford, United Kingdom,
3/09/03.
https://doi.org/10.1007/978-3-540-45224-9APA
Tasoulis, D. K., Spyridonos, P.
, Pavlidis, N. G., Cavouras, D., Ravazoula, P., Nikiforidis, G., & Vrahatis, M. N. (2003).
Urinary bladder tumor grade diagnosis using on-line trained neural networks. In
Knowledge-Based Intelligent Information and Engineering Systems: 7th INternational Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part I (Vol. 2773 PART 1, pp. 199-206). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). Springer.
https://doi.org/10.1007/978-3-540-45224-9Vancouver
Tasoulis DK, Spyridonos P
, Pavlidis NG, Cavouras D, Ravazoula P, Nikiforidis G et al.
Urinary bladder tumor grade diagnosis using on-line trained neural networks. In Knowledge-Based Intelligent Information and Engineering Systems: 7th INternational Conference, KES 2003, Oxford, UK, September 2003. Proceedings, Part I. Vol. 2773 PART 1. Springer. 2003. p. 199-206. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/978-3-540-45224-9
Author
Bibtex
@inproceedings{ad90ae1d42be4a9b92341a1bc6b564cb,
title = "Urinary bladder tumor grade diagnosis using on-line trained neural networks",
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 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.",
author = "Tasoulis, {D. K.} and P. Spyridonos and Pavlidis, {N. G.} and D. Cavouras and P. Ravazoula and G. Nikiforidis and Vrahatis, {M. N.}",
year = "2003",
month = dec,
day = "1",
doi = "10.1007/978-3-540-45224-9",
language = "English",
isbn = "9783540408031",
volume = "2773 PART 1",
series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",
publisher = "Springer",
pages = "199--206",
booktitle = "Knowledge-Based Intelligent Information and Engineering Systems",
note = "7th International Conference, KES 2003 ; Conference date: 03-09-2003 Through 05-09-2003",
}
RIS
TY - GEN
T1 - Urinary bladder tumor grade diagnosis using on-line trained neural networks
AU - Tasoulis, D. K.
AU - Spyridonos, P.
AU - Pavlidis, N. G.
AU - Cavouras, D.
AU - Ravazoula, P.
AU - Nikiforidis, G.
AU - Vrahatis, M. N.
PY - 2003/12/1
Y1 - 2003/12/1
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-540-45224-9
DO - 10.1007/978-3-540-45224-9
M3 - Conference contribution/Paper
AN - SCOPUS:8344289104
SN - 9783540408031
VL - 2773 PART 1
T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
SP - 199
EP - 206
BT - Knowledge-Based Intelligent Information and Engineering Systems
PB - Springer
T2 - 7th International Conference, KES 2003
Y2 - 3 September 2003 through 5 September 2003
ER -
