International Journal of Computational Intelligence Systems

Volume 14, Issue 1, 2021, Pages 715 - 722

Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks

Authors
Ahmet Haşim Yurttakal1, *, ORCID, Hasan Erbay2, ORCID, Gökalp Çinarer1, ORCID, Hatice Baş3, ORCID
1Computer Technologies Department, Bozok University, Yozgat, 66100, Turkey
2Computer Engineering Department, University of Turkish Aeronautical Association, Ankara, 06790, Turkey
3Department of Biology, Bozok University, Yozgat, 66200, Turkey
*Corresponding author. Email: ahmet.yurttakal@bozok.edu.tr
Corresponding Author
Ahmet Haşim Yurttakal
Received 28 September 2020, Accepted 6 November 2020, Available Online 17 November 2020.
DOI
10.2991/ijcis.d.201110.001How to use a DOI?
Keywords
Convolutional neural networks; Transfer learning; Classification; Histopathology
Abstract

Diabetes mellitus is a common disease worldwide. In progressive diabetes patients, deterioration of kidney histology tissue begins. Currently, the histopathologic examination of kidney tissue samples has been performed manually by pathologists. This examination process is time-consuming and requires pathologists' expertise. Thus, automatic detection methods are crucial for early detection and also treatment planning. Computer-aided diagnostic systems based on deep learning show high success rates in classifying medical images if a large and diverse image set is available during the training process. Herein, transfer learning-based convolutional neural network model was proposed for the automatic detection of diabetes mellitus using only rat kidney histopathology images. The model monitors structural changes, especially in the glomerulus and also other parts of the kidney caused by the damages of diabetes. According to the simulation results, the proposed model has reached 97.5% accuracy. As a result, the recommended model can quickly and accurately classify histopathology images and helps pathologists as the second reader in critical situations

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
715 - 722
Publication Date
2020/11/17
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.201110.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Ahmet Haşim Yurttakal
AU  - Hasan Erbay
AU  - Gökalp Çinarer
AU  - Hatice Baş
PY  - 2020
DA  - 2020/11/17
TI  - Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 715
EP  - 722
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201110.001
DO  - 10.2991/ijcis.d.201110.001
ID  - Yurttakal2020
ER  -