Recognition of Tomographic Images in the Diagnosis of Stroke

Kirill Kalmutskiy, Andrey Tulupov, Vladimir Berikov

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


In this paper, a method for automatic recognition of acute stroke model using non-contrast computed tomography brain images is presented. The complexity of the task lies in the fact that the dataset consists of a very small number of images. To solve the problem, we used the traditional computer vision methods and a convolutional neural network consisting of a segmentator and classifier. To increase the dataset, augmentations and sub images were used. Experiments with real CT images using validation and test samples showed that even on an extremely small dataset it is possible to train a model that will successfully cope with the classification and segmentation of images. We also proposed a way to increase the interpretability of the model.

Язык оригиналаанглийский
Название основной публикацииPattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
РедакторыAlberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
ИздательSpringer Science and Business Media Deutschland GmbH
Число страниц6
ISBN (печатное издание)9783030688202
СостояниеОпубликовано - 2021
Событие25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Италия
Продолжительность: 10 янв. 202111 янв. 2021

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том12665 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349


Конференция25th International Conference on Pattern Recognition Workshops, ICPR 2020

Предметные области OECD FOS+WOS



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