Model Ensembeling: A simple way of improving model performance for chromosome classification
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Date
2022
Authors
Pijáčková, Kristýna
Gotthans, Tomáš
Gotthans, Jakub
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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Abstract
This paper deals with chromosome classification via convolutional neural networks and model ensembling. Chromosome classification is a part of a procedure in karyotyping, where the chromosomes should be paired and ordered so that they are prepared for inspection of abnormalities. Model ensembling was used as a technique to improve overall classification accuracy by using all of the trained models. We achieved 94.8 \% accuracy for a Q-band BioImlab dataset and 97.48 \% for a G-band chromosome CIR dataset.
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Citation
Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 158-161. ISBN 978-80-214-6030-0
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
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Peer-reviewed
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií