Detailed Information on Publication Record

@inproceedings{33247,
   author = {Blahuta, Jiří and Soukup, Tomáš and Skácel, Jakub},
   address = {Cham},
   booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science},
   doi = {http://dx.doi.org/10.1007/978-3-030-01421-6_9},
   edition = {11140},
   editor = {Manolopoulos, Y., Hammer, B., Maglogiannis I., Kurkova V., Iliadis, L.},
   keywords = {Atherosclerotic plaque; Ultrasound; Expert system; Rule-based system; Image processing with ANN; B-image recognition},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-01421-6},
   pages = {90-100},
   publisher = {Springer Verlag},
   title = {Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research},
   url = {https://link.springer.com/chapter/10.1007%2F978-3-030-01421-6_9},
   year = {2018}
}

TY  - JOUR
ID  - 33247
AU  - Blahuta, Jiří - Soukup, Tomáš - Skácel, Jakub
PY  - 2018
TI  - Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research
PB  - Springer Verlag
CY  - Cham
SN  - 9783030014216
KW  - Atherosclerotic plaque
KW  - Ultrasound
KW  - Expert system
KW  - Rule-based system
KW  - Image processing with ANN
KW  - B-image recognition
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-01421-6_9
L2  - https://link.springer.com/chapter/10.1007%2F978-3-030-01421-6_9
N2  - Early diagnostics and knowledge of the progress of atherosclerotic plaques are key parameters which can help start the most efficient treatment. Reliable prediction of growing of atherosclerotic plaques could be very important part of early diagnostics to judge potential impact of the plaque and to decide necessity of immediate artery recanalization. For this pilot study we have a large set of measured data from total of 482 patients. For each patient the width of the plaque from left and right side during at least 5 years at regular intervals for 6 months was measured Patients were examined each 6 months and width of the plaque was measured using ultrasound B-image and the data were stored into a database. The first part is focused on rule-based expert system designed for evaluation of suggestion to immediate recanalization according to progress of the plaque. These results will be verified by an experienced sonographer. This system could be a starting point to design an artificial neural network with adaptive learning based on image processing of ultrasound B-images for classification of the plaques using feature analysis. The principle of the network is based on edge detection analysis of the plaques using feed-forwarded network with Error Back-Propagation algorithm. Training and learning of the ANN will be time-consuming processes for a long-term research. The goal is to create ANN which can recognize the border of the plaques and to measure of the width. The expert system and ANN are two different approaches, however, both of them can cooperate.
ER  - 

BLAHUTA, Jiří, Tomáš SOUKUP and Jakub SKÁCEL. Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research. Online. In Manolopoulos, Y., Hammer, B., Maglogiannis I., Kurkova V., Iliadis, L. \textit{Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science}. 11140th ed. Cham: Springer Verlag, 2018, p.~90-100. ISBN~978-3-030-01421-6. Available from: https://dx.doi.org/10.1007/978-3-030-01421-6\_{}9.