Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks

Authors

Ahmet Haşim Yurttakal^{1, *,} , Hasan Erbay^2, , Gökalp Çinarer^1, , Hatice Baş^3,

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

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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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  -