An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images

Devrim Akgün

doi:10.35377/saucis.03.03.725647

TR EN

An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images

Abstract

Deep learning networks has become an important tool for image classification applications. Distortions on images may cause the performance of a classifier to decrease significantly. In the present paper, a comparative investigation for binary classification performance of VGG16 network under corrupted inputs has been presented. For this purpose, images corrupted at various levels and fixed levels with Gaussian noise, Salt and Pepper noise and blur effect were used for testing. Convolutional layers of the VGG16 were frozen except the last three convolutional layers and a dense layer for binary classification was added. According to experimental results, as the effect of distortion is increased, performance of the deep learning classifier drops significantly. In the case of augmented training with distortion effects, the results were improved significantly.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Devrim Akgün ^*
0000-0002-0770-599X
Türkiye

Publication Date

December 30, 2020

Submission Date

April 22, 2020

Acceptance Date

December 2, 2020

Published in Issue

Year 2020 Volume: 3 Number: 3

DOI

https://doi.org/10.35377/saucis.03.03.725647

IZ

https://izlik.org/JA56KX45CR

Cite

RIS / Bibtex

APA

Akgün, D. (2020). An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images. Sakarya University Journal of Computer and Information Sciences, 3(3), 264-271. https://doi.org/10.35377/saucis.03.03.725647

AMA

1.Akgün D. An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images. SAUCIS. 2020;3(3):264-271. doi:10.35377/saucis.03.03.725647

Chicago

Akgün, Devrim. 2020. “An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images”. Sakarya University Journal of Computer and Information Sciences 3 (3): 264-71. https://doi.org/10.35377/saucis.03.03.725647.

EndNote

Akgün D (December 1, 2020) An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images. Sakarya University Journal of Computer and Information Sciences 3 3 264–271.

IEEE

[1]D. Akgün, “An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images”, SAUCIS, vol. 3, no. 3, pp. 264–271, Dec. 2020, doi: 10.35377/saucis.03.03.725647.

ISNAD

Akgün, Devrim. “An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images”. Sakarya University Journal of Computer and Information Sciences 3/3 (December 1, 2020): 264-271. https://doi.org/10.35377/saucis.03.03.725647.

JAMA

1.Akgün D. An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images. SAUCIS. 2020;3:264–271.

MLA

Akgün, Devrim. “An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images”. Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, Dec. 2020, pp. 264-71, doi:10.35377/saucis.03.03.725647.

Vancouver

1.Devrim Akgün. An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images. SAUCIS. 2020 Dec. 1;3(3):264-71. doi:10.35377/saucis.03.03.725647

An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images

VGG16 İkili Sınıflandırıcı Derin Sinir Ağının Gürültülü ve Bulanık Görüntüler için Değerlendirilmesi

Öz

An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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