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

References

[1] Öztürk, V. (2008). Papilödem, psödopapilödem, disk ödem ve optik atrofi olgularında optik disk morfolojisinin heidelberg retina tomografisi ile kantitaf değerlendirilmesi, Yüksek lisans tezi, Başkent Üniversitesi.pp. 1-85.
[2] İbrahimov, E. (2009). Optik disk kabarıklığında retina sinir lifi tabakası kalınlığının OCT ve HRT ile değerlendirilmesi, Yüksek lisans tezi, Dokuz Eylül Üniversitesi, pp. 1-49.
[3] Şimşek, F., Bilge, N., Ceylan, M. (2019). Erzurum ve çevre illerde psödotümör serebri tanısı ile takip edilen hastaların klinik ve demografik verileri, Harran Üniversitesi Tıp Fakültesi Dergisi, 16(2), 331-335.
[4] Oyar O., (2008). Magnetik rezonans görüntüleme MRG nin klinik uygulamaları ve endikasyonları, Harran Üniversitesi Tıp Fakültesi Dergisi, 5(2), 31-40.
[5] Sarıoğlu, B. (2012). Türkiyede MR ve BT görüntüleme işlemlerinin Sosyal Güvenlik Kurumuna ekonomik yükünün değerlendirilmesi, Yüksek lisans tezi, Başkent Üniversitesi, 1-98.
[6] Çifcibaşı, F. (2017). Aci̇l servi̇ste pedi̇atri̇k hastalarda lomber ponksi̇yon yeri̇ni̇n yatak başı ultrason i̇le beli̇rlenmesi̇ni̇n etki̇nli̇ği̇, Yüksek lisans tezi, Pamukkale Üniversitesi.
[7] Çelik, D. (2013). Çocuklarda lomber ponksiyon iğne derinliğinin tahmin edilmesi, Yüksek lisans tezi, Selçuk Üniversitesi.
[8] Gómez-Valverde J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., Sánchez, C.I. & Ledesma-Carbayo, M.J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning, Biomedical Optics Express, 10(2), 892-913. DOI: 10.1364/BOE.10.000892

[9] Liu, T.Y.A., Wei, J., Zhu, H., Subramanian, P.S., Myung, D., Paul, H.Y., Hui, F.K., Unberath, M., Ting, D.S.W. & Miller, N.R. (2021). Detection of optic disc abnormalities in color fundus photographs using deep learning, Journal of Neuro-Ophthalmology, 41(3), 368-374. DOI: 10.1097/WNO.0000000000001358
[10] Milea, D., Najjar, R.P., Jiang, Z., Ting, D., Vasseneix, C., Xu, X., Fard, M.A., Fonseca, P., Vanikieti, K., Lagrèze W.A., Morgia, C.L., Cheung, C.Y., Hamann, S., Chiquet, C., Sanda, N., Yang, H., Mejico, L.J., Rougier, M.B., Kho, R., Tran, T.H.C., Singhal, S., Gohier, P., Clermont-Vignal, C., Cheng, C.Y., Jonas, J.B., Yu-Wai-Man, P., Fraser, C. L., Chen, J.J., Ambika, S., Miller, N.R., Liu, Y., Newman, N.J., Wong, T.Y. & Biousse V. (2020). Artificial intelligence to detect papilledema from ocular fundus photographs, New England Journal of Medicine, 382(18), 1687-1695. DOI: 10.1056/NEJMoa1917130
[11] Vasseneix, C., Najjar, R.P., Xu, X., Tang, Z., Loo, J.L., Singhal, S., Tow, S., Milea, L., Ting, D.S.W., Liu, Y., Wong, T.Y., Newman, N.J., Biousse, V., Milea, D. & BONSAI Group (2021). Accuracy of a deep learning system for classification of papilledema severity on ocular fundus photographs. Neurology, 97(4), e369-e377. DOI: 10.1212/WNL.0000000000012226
[12] Bakır, H., Yılmaz, Ş. (2022). Using transfer learning technique as a feature extraction phase for diagnosis of cataract disease in the eye, International Journal of Sivas University of Science and Technology, 1(1), 17-33.
[13] Ahn, J.M., Kim S., Ahn, K.S., Cho, S.H., Kim, U.S. (2019). Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema. BMC Ophthalmology, 19(1), 178. The dataset can be downloaded from: https://osf.io/2w5ce/ DOI: 10.1186/s12886-019-1184-0
[14] Arısoy, M.Ö. & Dikmen, Ü. (2014). Manyetik belirti haritalarının histogram eşitleme yöntemi kullanılarak iyileştirilmesi, Yerbilimleri, 35(2), 141-168.
[15] Bozkurt, H. & Çelebi, A.T. (2021). Ortalama filtre kullanılarak termal görüntülerde sayısal detay iyileştirme, International Marmara Sciences Congress, Proceedings Book, pp. 103-108.
[16] Tan, M., Le, Q.V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks, 36th Int. Conf. Mach. Learn, ICML 2019, vol. 2019-June, pp. 10691–10700.
[17] Ahmed, T., Sabab, N.H.N. (2022). Classification and understanding of cloud structures via satellite images with EfficientUNet, SN Computer Science, 3, 99. DOI: 10.1007/s42979-021-00981-2
[18] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks, In Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, USA, 2018), pp. 4510-4520.
[19] He, K., Zhang X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, (IEEE, USA, 2016) pp. 770-778.
[20] Aksoy, B., Salman, O.K.M. (2022). Prediction of Covid-19 disease with resnet-101 deep learning architecture using computerized tomography images. Turkish Journal of Nature and Science, 11(2), 36-42. DOI: 10.46810/tdfd.1095624
[21] Chung, Y.M., Hu, C.S., Lawson, A., Smyth, C. (2021). Toporesnet: A hybrid deep learning architecture and its application to skin lesion classification, Mathematics, 9(22), 2924. DOI: 10.1109/BigData.2018.8622175.
[22] Tong, Y., Lu, W., Deng, Q.Q., Chen, C., Shen, Y. (2020). Automated identification of retinopathy of prematurity by image-based deep learning, Eye and Vision, 7, 40. DOI: 10.1186/s40662-020-00206-2
[23] İnik, Ö., Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri, Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
[24] Kishore, A., Singh, S. (2015). Natural language image descriptor, 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (IEEE, India, 2015), pp. 110-115. DOI: 10.1109/RAICS.2015.7488398
[25] Eryılmaz, F., Karacan, H. (2021). Akciğer X-Ray görüntülerinden COVID-19 tespitinde hafif ve geleneksel evrişimsel sinir ağ mimarilerinin karşılaştırılması, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(6), 26-39. DOI: 10.29130/dubited.1011829
[26] Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., Hemanth, J. (2021). SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2, Sustainable Cities and Society, 66, 102692. DOI: 10.1016/j.scs.2020.102692
[27] Ba Alawi, A.E., Qasem, A.M. (2021). Lightweight CNN-based models for masked face recognition, In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (IEEE, Yemen, 2021), pp. 1-5. DOI: 10.1109/ICOTEN52080.2021.9493424
[28] Wang, J. (2020). Anomaly detection of arm X-Ray based on deep learning, In IOP Conference Series: Earth and Environmental Science, 440(4), 042056. DOI: 10.1088/1755-1315/440/4/042056
[29] Zeng, G. (2020). On the confusion matrix in credit scoring and its analytical properties, Communications in Statistics-Theory and Methods, 49(9), 2080-2093. DOI: 10.1080/03610926.2019.1568485
[30] Çelik, S., & Kasım, Ö. (2020). Detection of tumor slice in brain magnetic resonance images by feature optimized transfer learning. Aksaray University Journal of Science and Engineering, 4(2) 187-198. DOI: 10.29002/asujse.820599
[31] Akyol, S., Yıldırım, M. & Alataş, B. (2023). Multi-feature fusion and improved BO and IGWO metaheuristics based models for automatically diagnosing the sleep disorders from sleep sounds, Computers in Biology and Medicine, 157, 106768. DOI: 10.1016/j.compbiomed.2023.106768
[32] Bilgen, Ö.B. & Doğan, N. (2017). Puanlayıcılar arası güvenirlik belirleme tekniklerinin karşılaştırılması, Journal of Measurement and Evaluation in Education and Psychology, 8(1), 63-78. DOI: 10.21031/epod.294847
[33] Wardhani, N.W.S., Rochayani, M.Y., Iriany, A., Sulistyono, A.D. & Lestantyo, P. (2019). Cross-validation metrics for evaluating classification performance on imbalanced data, In 2019 International conference on computer, control, informatics and its applications (IC3INA), IEEE, (pp. 14-18). DOI: 10.1109/IC3INA48034.2019.8949568

Details

Primary Language

English

Subjects

Engineering , Bioelectronic

Journal Section

Research Article

Authors

Merve Kokulu
0009-0007-3593-9666
Türkiye

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

Publication Date

December 30, 2023

Submission Date

April 11, 2023

Acceptance Date

July 26, 2023

Published in Issue

Year 1970 Volume: 7 Number: 2

DOI

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

IZ

https://izlik.org/JA54GP95LZ

Cite

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