Classification of Microscopy Image Stained by Ziehl–Neelsen Method Using Different Architectures of Convolutional Neural Network

Описание

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

Конференция: 22nd International Conference on Neuroinformatics, 2020

Год издания: 2021

Идентификатор DOI: 10.1007/978-3-030-60577-3_32

Ключевые слова: convolutional neural network, image processing, ziehl-nielsen

Аннотация: Tuberculosis (TB) is a global issue of public health. The paper presents the result of the investigation of the clinical efficacy of a convolutional neural network for detection of acid-fast stained TB bacillus. The experimental set contains images of the results of microscopy of patients' sputum stained by the Ziehl–Neelsen methodПоказать полностью. During the experiment, the original set of images segmented to augmentation the data. We built a few convolutional neural networks (CNN) models to recognize TB bacillus by transfer learning. The experiment conducted based on AlexNet, VGGNet-19, ResNet-18, DenseNet, GoogLeNet-incept-v3, In-ceptionResNet-v2 and the classic three-layer model. The DenseNet is the most productive model of transfer learning on the experimental set. During the study, the usual three-layer convolution network developed, which showed the maximum value of accuracy in the experiment. A convolutional neural network with a simple structure may be an effective base for an automated detection system for stained TB bacilli, but image segmentation is required to increase recognition accuracy. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Издание

Журнал: Studies in Computational Intelligence

Выпуск журнала: Vol. 925 SCI

Номера страниц: 269-275

ISSN журнала: 1860949X

Издатель: Springer Science and Business Media Deutschland GmbH

Авторы

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

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