Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Merve Kokulu; Hanife Göker

doi:10.29002/asujse.1280766

EN

Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Abstract

Papilledema is edema in the area where the optic nerve meets the eye as a result of increased pressure inside the head. This disease can result in very serious problems, such as abnormal optical changes, decreased visual acuity, and even permanent blindness if left untreated. In this study, an image processing based solution was presented for the detection of papilledema severity from color fundus images using transfer learning approaches. The image dataset includes 295 papilledema images, 295 pseudopapilledema images, and 779 control images. Histogram equalization and the 3D box filter were used for image preprocessing. The images were enhanced with the histogram equalization method and denoised with the 3D box filter method. Then, the performances of EfficentNet-B0, GoogLeNet, MobileNetV2, NASNetMobile, and ResNet-101 transfer learning approaches were compared. The hold-out method was used to calculate the performance of transfer learning. In the experiments, the MobileNetV2 approach had the highest performance with 0.96 overall accuracy and 0.94 Cohen's Kappa. The results of the experiments proved that the combination of the histogram equalization, the 3D box filter, and the MobileNetV2 transfer learning approach can be used for automatic detection of papilledema severity. Compared to other similar studies that are known in the literature, the overall accuracy was higher.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik , Biyoelektronik

Bölüm

Araştırma Makalesi

Yazarlar

Merve Kokulu
0009-0007-3593-9666
Türkiye

Hanife Göker ^*
0000-0003-0396-7885
Türkiye

Yayımlanma Tarihi

30 Aralık 2023

Gönderilme Tarihi

11 Nisan 2023

Kabul Tarihi

26 Temmuz 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 7 Sayı: 2

DOI

https://doi.org/10.29002/asujse.1280766

IZ

https://izlik.org/JA54GP95LZ

Kaynak Göster

RIS / Bibtex

APA

Kokulu, M., & Göker, H. (2023). Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches. Aksaray University Journal of Science and Engineering, 7(2), 53-61. https://doi.org/10.29002/asujse.1280766

Cited By

An evaluation of AI-based methods for papilledema detection in retinal fundus images

Biomedical Signal Processing and Control

https://doi.org/10.1016/j.bspc.2024.106120

Unveiling AI's role in papilledema diagnosis from fundus images: A systematic review with diagnostic test accuracy meta-analysis and comparison of human expert performance

Computers in Biology and Medicine

https://doi.org/10.1016/j.compbiomed.2024.109350

Öz

Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

An evaluation of AI-based methods for papilledema detection in retinal fundus images

Unveiling AI's role in papilledema diagnosis from fundus images: A systematic review with diagnostic test accuracy meta-analysis and comparison of human expert performance