Research Article


DOI :10.26650/acin.1173465   IUP :10.26650/acin.1173465    Full Text (PDF)

A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images

Onur Sevli

Corneal ulcer is a common disease worldwide and is one of the leading causes of corneal blindness. Diagnosis of the disease requires expertise, and the number of experienced ophthalmologists is not sufficient, especially in underdeveloped countries. For this reason, it is necessary to develop technology-based decision support systems in the diagnosis of the disease. However, the number of studies on this subject is not sufficient. In this study, CNN-based classifications were performed using corneal ulcer images obtained by an ocular staining technique, consisting of 712 samples and three classes. In addition to the AlexNet and VGG16 state-of-the-art architectures, which are widely used in the literature, a CNN model proposed for this study was used for classification. In the classifications performed by applying data augmentation, 95.34% accuracy with AlexNet, 98.14% with VGG16, and 100% accuracy with the proposed model was obtained. The findings were compared with similar studies in the literature. It was concluded that the accuracy rates obtained with all of the models used in the study were generally higher than similar studies in the literature, and the accuracy obtained with the proposed CNN model was higher than all of the peers. In addition, the success of the proposed model compared to other models with more complex structures revealed that it is not always necessary to use complex architectures for high accuracy.

DOI :10.26650/acin.1173465   IUP :10.26650/acin.1173465    Full Text (PDF)

Oküler Boyama Görüntülerinde Kornea Ülserinin Teşhisi İçin Derin Öğrenmeye Dayalı Bir Sınıflandırma Çalışması

Onur Sevli

Kornea ülseri dünya genelinde yaygın görülen bir hastalık olup kornea körlüğünün önce gelen nedenlerindendir. Hastalığın teşhisi uzmanlık gerektirmekte olup, özellikle az gelişmiş ülkelerde tecrübeli oftalmolog sayısı yeterli sayıda değildir. Bu durum hastalığın teşhisinde etkin ve uzmanlara destek sistemlerin oluşturulmasını gerekli kılmaktadır. Ancak henüz bu konuda yapılmış olan çalışmaların sayısı yeterli düzeyde değildir. Bu çalışmada 712 adet ve 3 türden oluşan, oküler boyama tekniği ile elde edilen kornea ülser görüntüsü kullanılarak CNN tabanlı sınıflandırmalar gerçekleştirilmiştir. Literatürde yaygın kullanılan AlexNet ve VGG16 daha derin state-of-art mimarileri yanında bu çalışma için önerilen bir CNN modeli kullanılmıştır. Veri arttırımı uygulanarak gerçekleştirilen sınıflandırmalarda AlexNet ile 95.34%, VGG16 ile 98.14%, ve önerilen model ile 100% doğruluk elde edilmiştir. Elde edilen bulgular literatürdeki benzer çalışmalarda karşılaştırılmıştır. Tüm modeller ile elde edilen doğruluk oranlarının literatürdeki çalışmaların genelinden yüksek olduğu, önerilen CNN modeli ile elde edilen doğruluğun ise emsallerin tamamından yüksek olduğu sonucuna ulaşılmıştır. Ayrıca önerilen modelin daha karmaşık yapıdaki diğer modellere nazaran da yüksek başarı sergilemiş olması, daha minimal mimarilerle de yüksek başarı elde edilebileceğini ortaya koymuştur.


PDF View

References

  • Akram, A., & Debnath, R. (2019). An Efficient Automated Corneal Ulcer Detection Method using Convolutional Neural Network. 2019 22nd International Conference on Computer and Information Technology (ICCIT), 1-6. google scholar
  • Aksoy, B. (2021). Estimation of Energy Produced in Hydroelectric Power Plant Industrial Automation Using Deep Learning and Hybrid Machine Learning Techniques. Electric Power Components and Systems, 49(3), 213-232. https://doi.org/10.1080/15325008.2021.1937401 google scholar
  • Amescua, G., Miller, D., & Alfonso, E. C. (2012). What is causing the corneal ulcer? Management strategies for unresponsive corneal ulceration. Eye, 26(2), 228-236. https://doi.org/10.1038/eye.2011.316 google scholar
  • Basak, S. K., Basak, S., Mohanta, A., & Bhowmick, A. (2005). Epidemiological and microbiological diagnosis of suppurative keratitis in Gangetic West Bengal, eastern India. Indian Journal of Ophthalmology, 53(1), 17-22. google scholar
  • Bron, A. J., Argüeso, P., Irkec, M., & Bright, F. V. (2015). Clinical staining of the ocular surface: Mechanisms and interpretations. Progress in Retinal and Eye Research, 44, 36-61. https://doi.org/10.1016/j.preteyeres.2014.10.001 google scholar
  • Chen, J., & Yuan, J. (2010). Strengthen the study of the ocular surface reconstruction. Chinese Journal of Ophthalmology, 46(1), 3-5. google scholar
  • Cohen, E. J., Laibson, P. R., Arentsen, J. J., & Clemons, C. S. (1987). Corneal ulcers associated with cosmetic extended wear soft contact lenses. Ophthalmology, 94(2), 109-114. google scholar
  • Deng, L., Lyu, J., Huang, H., Deng, Y., Yuan, J., & Tang, X. (2020). The SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers. Scientific Data, 7(1), 23. https://doi.org/10.1038/s41597-020-0360-7 google scholar
  • Diamond, J., Leeming, J., Coombs, G., Pearman, J., Sharma, A., Illingworth, C., Crawford, G., & Easty, D. (1999). Corneal biopsy with tissue micro homogenisation for isolation of organisms in bacterial keratitis. Eye, 13(4), 545-549. google scholar
  • Garg, P., & Rao, G. N. (1999). Corneal ulcer: Diagnosis and management. Community Eye Health, 12(30), 21-23. PubMed. google scholar
  • Gross, J., Breitenbach, J., Baumgartl, H., & Buettner, R. (2021). High-performance detection of corneal ulceration using image classification with convolutional neural networks. E 54th Hawaii International Conference on System Sciences, 3416-3425. google scholar
  • Katara, R. S., Patel, N. D., & Sinha, M. (2013). A clinical microbiological study of corneal ulcer patients at western Gujarat, India. Acta Medica Iranica, 399-403. google scholar
  • Kim, J. Y., Lee, H. E., Choi, Y. H., Lee, S. J., & Jeon, J. S. (2019). CNN-based diagnosis models for canine ulcerative keratitis. Scientific Reports, 9(1), 1-7. google scholar
  • Krizhevsky, A., Sutskever, I.,& Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. google scholar
  • Li, Z., Jiang, J., Chen, K., Chen, Q., Zheng, Q., Liu, X., Weng, H., Wu, S., & Chen, W. (2021). Preventing corneal blindness caused by keratitis using artificial intelligence. Nature Communications, 12(1), 1-12. google scholar
  • Maurice, D. M. (1957). The structure and transparency of the cornea. The Journal of Physiology, 136(2), 263. google scholar
  • Portela, H. M. B., MS Veras, R. de, Vogado, L. H. S., Leite, D., Sousa, J. A. de, Paiva, A. C. de, & Tavares, J. M. R. (2021). A Coarse to Fine Corneal Ulcer Segmentation Approach Using U-net and DexiNed in Chain. Iberoamerican Congress on Pattern Recognition, 13-23. google scholar
  • Saini, J. S., Jain, A. K., Kumar, S., Vikal, S., Pankaj, S., & Singh, S. (2003). Neural network approach to classify infective keratitis. Current Eye Research, 27(2), 111-116. google scholar
  • Sajeev, S., & Prem Senthil, M. (2021). Classifying infective keratitis using a deep learning approach. 2021 Australasian Computer Science Week Multiconference, 1-4. google scholar
  • Simonyan, K., & Zisserman, A. (2014). Very Deep ConvNets for Large-Scale Image Recognition. CoRR. google scholar
  • Song, X., Xie, L., Tan, X., Wang, Z., Yang, Y., Yuan, Y., Deng, Y., Fu, S., Xu, J., Sun, X., & others. (2014). A multi-center, cross-sectional study on the burden of infectious keratitis in China. PLoS One, 9(12), e113843. google scholar
  • Tang, N., Liu, H., Yue, K., Li, W., & Yue, X. (2020). Automatic classification for corneal ulcer using a modified VGG network. 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 120-123. google scholar
  • Teeyapan, K. (2021). Deep learning-based approach for corneal ulcer screening. The 12th International Conference on Computational Systems-Biology and Bioinformatics, 27-36. google scholar
  • Wang, T., Wang, M., Zhu, W., Wang, L., Chen, Z., Peng, Y., Shi, F., Zhou, Y., Yao, C., & Chen, X. (2021). Semi-MsST-GAN: A Semi-Supervised Segmentation Method for Corneal Ulcer Segmentation in Slit-Lamp Images. Frontiers in Neuroscience, 15. google scholar
  • Wang, T., Zhu, W., Wang, M., Chen, Z., & Chen, X. (2021). Cu-segnet: Corneal ulcer segmentation network. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1518-1521. google scholar
  • Wang, Z., Lyu, J., Luo, W., & Tang, X. (2021). Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation. International Workshop on Ophthalmic Medical Image Analysis, 1-10. google scholar
  • Xu, Y., Kong, M., Xie, W., Duan, R., Fang, Z., Lin, Y., Zhu, Q., Tang, S., Wu, F., & Yao, Y.-F. (2021). Deep sequential feature learning in clinical image classification of infectious keratitis. Engineering, 7(7), 1002-1010. google scholar

Citations

Copy and paste a formatted citation or use one of the options to export in your chosen format


EXPORT



APA

Sevli, O. (2023). A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images. Acta Infologica, 7(2), 281-292. https://doi.org/10.26650/acin.1173465


AMA

Sevli O. A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images. Acta Infologica. 2023;7(2):281-292. https://doi.org/10.26650/acin.1173465


ABNT

Sevli, O. A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images. Acta Infologica, [Publisher Location], v. 7, n. 2, p. 281-292, 2023.


Chicago: Author-Date Style

Sevli, Onur,. 2023. “A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images.” Acta Infologica 7, no. 2: 281-292. https://doi.org/10.26650/acin.1173465


Chicago: Humanities Style

Sevli, Onur,. A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images.” Acta Infologica 7, no. 2 (Mar. 2024): 281-292. https://doi.org/10.26650/acin.1173465


Harvard: Australian Style

Sevli, O 2023, 'A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images', Acta Infologica, vol. 7, no. 2, pp. 281-292, viewed 3 Mar. 2024, https://doi.org/10.26650/acin.1173465


Harvard: Author-Date Style

Sevli, O. (2023) ‘A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images’, Acta Infologica, 7(2), pp. 281-292. https://doi.org/10.26650/acin.1173465 (3 Mar. 2024).


MLA

Sevli, Onur,. A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images.” Acta Infologica, vol. 7, no. 2, 2023, pp. 281-292. [Database Container], https://doi.org/10.26650/acin.1173465


Vancouver

Sevli O. A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images. Acta Infologica [Internet]. 3 Mar. 2024 [cited 3 Mar. 2024];7(2):281-292. Available from: https://doi.org/10.26650/acin.1173465 doi: 10.26650/acin.1173465


ISNAD

Sevli, Onur. A Deep Learning-Based Classification Study for Diagnosing Corneal Ulcers on Ocular Staining Images”. Acta Infologica 7/2 (Mar. 2024): 281-292. https://doi.org/10.26650/acin.1173465



TIMELINE


Submitted10.09.2022
Accepted12.06.2023
Published Online14.08.2023

LICENCE


Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.