Comparative Analysis of Deep Neural Network and Texture-Based Classifiers for Recognition of Acute Stroke using Non-Contrast CT Images

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

This work presents a computer technology for automatic recognition of acute stroke using non-contrast computed tomography brain images. The early diagnosis of acute stroke is of primary importance for deciding on a method for further treatment, and the developed system aims at assisting a radiology specialist in the decision making process. We consider deep neural network and texture-based classifiers in order to compare their efficiency on a moderate-sized sample of patients with acute stroke. We use U-net as a basic architecture of the neural network, and Haralick textural features, extracted from images, for kNN, SVM, Random Forest and Adaboost classifiers. Experiments with real CT images using cross-validation technique show that deep neural network outperforms the considered texture-based classifiers; however, the latter are faster in training. We demonstrate that texture-based approach is able to give potentially useful additional information for stroke recognition, such as estimates of textural features importance; visualization of differences in positive and negative class distributions.

Язык оригиналаанглийский
Название основной публикацииProceedings - 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы376-379
Число страниц4
ISBN (электронное издание)9781728131658
DOI
СостояниеОпубликовано - 1 мая 2020
Событие2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020 - Yekaterinburg, Российская Федерация
Продолжительность: 14 мая 202015 мая 2020

Серия публикаций

НазваниеProceedings - 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020

Конференция

Конференция2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020
СтранаРоссийская Федерация
ГородYekaterinburg
Период14.05.202015.05.2020

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    Nedel'ko, V., Kozinets, R., Tulupov, A., & Berikov, V. (2020). Comparative Analysis of Deep Neural Network and Texture-Based Classifiers for Recognition of Acute Stroke using Non-Contrast CT Images. В Proceedings - 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020 (стр. 376-379). [9117784] (Proceedings - 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT48449.2020.9117784