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research-article

Ensemble of fine‐tuned convolutional neural networks for urine sediment microscopic image classification

Authors:

Weiguo GongAuthors Info & Claims

IET Computer Vision, Volume 14, Issue 1

Pages 18 - 25

https://doi.org/10.1049/iet-cvi.2018.5829

Published: 20 January 2020 Publication History

Abstract

In this study, an ensemble of fine‐tuned convolutional neural networks (CNNs) is proposed. As CNN training requires large annotated data, which are lacking in the field of urine sediment microscopic image processing, the authors first pre‐trained the CNNs, including ResNet50 and GoogLeNet, and developed AlexNet on an ImageNet dataset. Thereafter, some of the weights of the pre‐trained CNNs were transferred to the urine sediment microscopic image dataset. To guide fine‐tuning of the learning rate and cascading features, the hierarchical nature of features in different convolutional layers was investigated by visualising the CNN. Then, they combined three CNNs as an ensemble of CNNs to decrease the differences and impurity interference among features of urine sediment microscopic image. These fusion features were employed to train the fully connected neural network for classification. In this study, they improved the accuracy of each CNN by an average of 2.2% through fine‐tuning of the learning rate and cascading features. Moreover, the better experimental results were achieved compared with other state‐of‐the‐art methods and indicated that a 97% classification accuracy can be attained.

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Cited By

Tuncer SÇınar AErkuş MTuncer T(2024)Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary SedimentApplied Computer Systems10.2478/acss-2024-000529:1(35-44)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.2478/acss-2024-0005
Sharma RMahanti GChakraborty CPanda GRath A(2023)An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer DetectionACM Transactions on Internet Technology10.1145/3637062Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3637062
Erten MBarua PTuncer IDogan SBaygin MTuncer TTan RAcharya U(2023)Swin-LBP: a competitive feature engineering model for urine sediment classificationNeural Computing and Applications10.1007/s00521-023-08919-w35:29(21621-21632)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1007/s00521-023-08919-w

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Information

Published In

cover image IET Computer Vision

IET Computer Vision Volume 14, Issue 1

February 2020

47 pages

EISSN:1751-9640

DOI:10.1049/cvi2.v14.1

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© 2020 The Institution of Engineering and Technology.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 20 January 2020

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Cited By

Tuncer SÇınar AErkuş MTuncer T(2024)Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary SedimentApplied Computer Systems10.2478/acss-2024-000529:1(35-44)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.2478/acss-2024-0005
Sharma RMahanti GChakraborty CPanda GRath A(2023)An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer DetectionACM Transactions on Internet Technology10.1145/3637062Online publication date: 11-Dec-2023
https://dl.acm.org/doi/10.1145/3637062
Erten MBarua PTuncer IDogan SBaygin MTuncer TTan RAcharya U(2023)Swin-LBP: a competitive feature engineering model for urine sediment classificationNeural Computing and Applications10.1007/s00521-023-08919-w35:29(21621-21632)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1007/s00521-023-08919-w

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