BLAHUTA, Jiří, Tomáš SOUKUP a 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. In Manolopoulos, Y., Hammer, B., Maglogiannis I., Kurkova V., Iliadis, L. Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science. 11140. vyd. Cham: Springer Verlag. s. 90-100. ISBN 978-3-030-01421-6. doi:10.1007/978-3-030-01421-6_9. 2018.
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Základní údaje
Originální název Pilot design of a rule-based system and an artificial neural network to risk evaluation of atherosclerotic plaques in long-range clinical research
Autoři BLAHUTA, Jiří (203 Česká republika, garant, domácí), Tomáš SOUKUP (203 Česká republika) a Jakub SKÁCEL (203 Česká republika, domácí).
Vydání 11140. vyd. Cham, Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science, od s. 90-100, 11 s. 2018.
Nakladatel Springer Verlag
Další údaje
Originální jazyk angličtina
Typ výsledku Stať ve sborníku
Obor 20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Stát vydavatele Německo
Utajení není předmětem státního či obchodního tajemství
Forma vydání elektronická verze "online"
WWW URL
Kód RIV RIV/47813059:19240/18:A0000220
Organizační jednotka Filozoficko-přírodovědecká fakulta v Opavě
ISBN 978-3-030-01421-6
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-030-01421-6_9
Klíčová slova anglicky Atherosclerotic plaque; Ultrasound; Expert system; Rule-based system; Image processing with ANN; B-image recognition
Štítky ÚI
Příznaky Recenzováno
Návaznosti LQ1602, projekt VaV.
Změnil Změnil: Mgr. Kamil Matula, Ph.D., učo 7389. Změněno: 18. 2. 2022 10:43.
Anotace
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.
VytisknoutZobrazeno: 28. 3. 2024 23:34