Optimizing Convolutional Neural Network Architecture for Microscopy Image Recognition for Tuberculosis Diagnosis

Описание

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Neuroinformatics, 2021

Год издания: 2022

Идентификатор DOI: 10.1007/978-3-030-91581-0_27

Ключевые слова: convolutional neural network, hyperparameter, optimization, ziehl-neelsen

Аннотация: Globally, tuberculosis (TB) is the leading infectious killer in the world before pandemia. This paper presents the result of optimizing convolutional neural network architecture for the detection of acid-fast stained TB bacillus. The experimental set contains the segmentation results of microscopy images of the patients sputum staiПоказать полностьюned by the Ziehl–Neelsen method. The authors constructed an experimental algorithm for optimizing the original convolutional neural network model, including optimizing the model dimension, data augmentation, adjusting the model parameters, and improving regularization. The authors built few models of convolutional neural networks (CNN) models to recognize TB bacillus, which showed the maximum value of metrics in the experiment. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Издание

Журнал: Studies in Computational Intelligence

Выпуск журнала: Vol. 1008 SCI

Номера страниц: 204-209

ISSN журнала: 1860949X

Издатель: Springer Science and Business Media Deutschland GmbH

Персоны

  • Udegova E.S. (Krasnoyarsk State Medical University named after Professor V.F. Voino-Yasenetsky, 1, Partizan Zheleznyak ave., Krasnoyrsk, 660022, Russian Federation)
  • Shelomentseva I.G. (Krasnoyarsk State Medical University named after Professor V.F. Voino-Yasenetsky, 1, Partizan Zheleznyak ave., Krasnoyrsk, 660022, Russian Federation)
  • Chentsov S.V. (Siberian Federal University, 79, Svobodny ave., Krasnoyrsk, 660041, Russian Federation)

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