Research Article
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Year 2022, Volume: 11 Issue: 4, 1084 - 1092, 31.12.2022
https://doi.org/10.17798/bitlisfen.1174512

Abstract

References

  • [1] Muramatsu C, Nakagawa T, Sawada A, Hatanaka Y, Yamamoto T, Fujita H. “Automated determination of cup-to-disc ratio for classification of glaucomatous and normal eyes on stereo retinal fundus images”. J Biomed Opt., 16(9), 2011.
  • [2] Issac A, Partha SM, Dutta MK. “An adaptive threshold-based image processing technique for improved glaucoma detection and classification”. Computer Methods and Programs in Biomedicine, 122(2):229–244, 2015
  • [3] Divya L, Jacob J. “Performance analysis of glaucoma detection approaches from fundus images”. Procedia Computer Science, 143:544–551. 8th International Conference on Advances in Computing and Communications (ICACC-2018)
  • [4] Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y. “Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis”. Symmetry, 10(4),2018.
  • [5] Gómez-Valverde JJ, Antón A, Fatti G, Liefers B, Herranz A, Santos A, ... Ledesma-CMJ. “Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning”. Biomedical optics express, 10(2), 892-913,2019.
  • [6] Yu S, Xiao D, Frost S, Kanagasingam Y. “Robust optic disc and cup segmentation with deep learning for glaucoma detection”. Computerized Medical Imaging and Graphics, 74:61–71,2019
  • [7] Claro M, Veras R, Santana A, Araujo F, Silva R, Almeida, J, Leite D. “An hybrid feature space from texture information and transfer learning for glaucoma classification”. Journal of Visual Communication and Image Representation, 64:102597,2019.
  • [8] Bisneto TRV, de Carvalho FAO, Magalhaes DMV. “Generative Adversarial network and texture features applied to automatic glaucoma detection”. Appl. Soft Comput., 90:106165,2020
  • [9] Pruthi J, Khanna K, Arora S. “Optic cup segmentation from retinal fundus images using glowworm swarm optimization for glaucoma detection”. Biomedical Signal Processing and Control, 60:102004, 2020.
  • [10] Nayak DR, Das D, Majhi B, Bhandary SV, Acharya UR. “Ecnet: An evolutionary convolutional network for automated glaucoma detection using fundus images”. Biomedical Signal Processing and Control, 67:102559, 2021.
  • [11] Mrad Y, Elloumi Y, Akil M, Bedoui M. “A fast and accurate method for glaucoma screening from smartphone-captured fundus images”. IRBM,2021.
  • [12] Diaz-Pinto A, Morales S, Naranjo V, Kohler T, Mossi JM, Navea A. (2019). “Cnns for automatic glaucoma assessment using fundus images: an extensive validation”. BioMed Eng OnLine,18(29), 2019.
  • [13] Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar RB, Zimmerman JB, Zuiderveld K. “Adaptive histogram equalization and its variations”. Computer Vision, Graphics, and Image Processing, 39(3):355–368, 1987.
  • [14] dos Santos JCM, Carrijo GA, de Fátima dos SCC., Ferreira JC, Sousa PM, Patrocínio AC. “Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter”. Research on Biomedical Engineering, 36(2), 107-119, 2020.
  • [15] Sonali SS, Singh AK, Ghrera S, Elhoseny M. “An approach for de-noising and contrast enhancement of retinal fundus image using clahe”. Optics and Laser Technology, 110:87–98, 2019.
  • [16] Toptaş B, Hanbay D. “Retinal blood vessel segmentation using pixel-based feature vector”. Biomedical Signal Processing and Control, 70:103053, 2021.
  • [17] Uysal E, Güraksin G. “Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks”. Multimed Tools Appl, 80, 2021.
  • [18] Tan M, Le Q. “Efficientnet: Rethinking model scaling for convolutional neural networks”. In International conference on machine learning (pp. 6105-6114). PMLR.,2019.
  • [19] Gupta N, Garg H, Agarwal R. “A robust framework for glaucoma detection using CLAHE and EfficientNet”. The Visual Computer, 1-14,2021.
  • [20] Toptaş B, Toptaş M, Hanbay D. “Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space”. Journal of Digital Imaging, 1-18,2022.
  • [21] Azzopardi G, Strisciuglio N, Vento M, Petkov N. “Trainable COSFIRE filters for vessel delineation with application to retinal images”. Medical image analysis, 19(1), 46-57,2015.
  • [22] Elangovan P, Nath MK. “Glaucoma assessment from color fundus images using convolutional neural network” International Journal of Imaging Systems and Technology, 31(2), 955-971,2021.
  • [23] Serte S., Serener A. “A generalized deep learning model for glaucoma detection”. In 2019 3rd International symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1-5). IEEE,2019.
  • [24] Christopher M., Nakahara K., Bowd C., Proudfoot J. A., Belghith A., Goldbaum M. H., ... Zangwill L. M. “Effects of study population, labeling and training on glaucoma detection using deep learning algorithms”. Translational vision science & technology, 9(2), 27-27,2020.
  • [25] Almeida-Galárraga D, Benavides-MK., Insuasti-Cruz E, Lovato-Villacís N, Suárez-Jaramillo V, Tene-Hurtado D, Tirado-Espín A, Villalba-Meneses GF. “Glaucoma detection through digital processing from fundus images using MATLAB”. In 2021 Second International Conference on Information Systems and Software Technologies (ICI2ST) (pp. 39-45). IEEE, 2021.

The Separation of glaucoma and non-glaucoma fundus images using EfficientNet-B0

Year 2022, Volume: 11 Issue: 4, 1084 - 1092, 31.12.2022
https://doi.org/10.17798/bitlisfen.1174512

Abstract

Glaucoma is an eye disease that causes vision loss. This disease progresses silently without symptoms. Therefore, it is a difficult disease to detect. If glaucoma is detected before it progresses to advanced stages, vision loss can be prevented. Computer-aided diagnosis systems are preferred to understand whether the fundus image contains glaucoma. These systems provide accurate classification of healthy and glaucoma images. In this article, a system to separate images of a fundus dataset as glaucoma or healthy is proposed. The EfficientNet B0 model, which is a deep learning model, is used in the proposed system. The input of this deep network model is designed as six layers. The experimental results of the designed model were obtained on the publicly available ACRIMA dataset images. In the end, the average accuracy rate is determined as 0.9775.

References

  • [1] Muramatsu C, Nakagawa T, Sawada A, Hatanaka Y, Yamamoto T, Fujita H. “Automated determination of cup-to-disc ratio for classification of glaucomatous and normal eyes on stereo retinal fundus images”. J Biomed Opt., 16(9), 2011.
  • [2] Issac A, Partha SM, Dutta MK. “An adaptive threshold-based image processing technique for improved glaucoma detection and classification”. Computer Methods and Programs in Biomedicine, 122(2):229–244, 2015
  • [3] Divya L, Jacob J. “Performance analysis of glaucoma detection approaches from fundus images”. Procedia Computer Science, 143:544–551. 8th International Conference on Advances in Computing and Communications (ICACC-2018)
  • [4] Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y. “Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis”. Symmetry, 10(4),2018.
  • [5] Gómez-Valverde JJ, Antón A, Fatti G, Liefers B, Herranz A, Santos A, ... Ledesma-CMJ. “Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning”. Biomedical optics express, 10(2), 892-913,2019.
  • [6] Yu S, Xiao D, Frost S, Kanagasingam Y. “Robust optic disc and cup segmentation with deep learning for glaucoma detection”. Computerized Medical Imaging and Graphics, 74:61–71,2019
  • [7] Claro M, Veras R, Santana A, Araujo F, Silva R, Almeida, J, Leite D. “An hybrid feature space from texture information and transfer learning for glaucoma classification”. Journal of Visual Communication and Image Representation, 64:102597,2019.
  • [8] Bisneto TRV, de Carvalho FAO, Magalhaes DMV. “Generative Adversarial network and texture features applied to automatic glaucoma detection”. Appl. Soft Comput., 90:106165,2020
  • [9] Pruthi J, Khanna K, Arora S. “Optic cup segmentation from retinal fundus images using glowworm swarm optimization for glaucoma detection”. Biomedical Signal Processing and Control, 60:102004, 2020.
  • [10] Nayak DR, Das D, Majhi B, Bhandary SV, Acharya UR. “Ecnet: An evolutionary convolutional network for automated glaucoma detection using fundus images”. Biomedical Signal Processing and Control, 67:102559, 2021.
  • [11] Mrad Y, Elloumi Y, Akil M, Bedoui M. “A fast and accurate method for glaucoma screening from smartphone-captured fundus images”. IRBM,2021.
  • [12] Diaz-Pinto A, Morales S, Naranjo V, Kohler T, Mossi JM, Navea A. (2019). “Cnns for automatic glaucoma assessment using fundus images: an extensive validation”. BioMed Eng OnLine,18(29), 2019.
  • [13] Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar RB, Zimmerman JB, Zuiderveld K. “Adaptive histogram equalization and its variations”. Computer Vision, Graphics, and Image Processing, 39(3):355–368, 1987.
  • [14] dos Santos JCM, Carrijo GA, de Fátima dos SCC., Ferreira JC, Sousa PM, Patrocínio AC. “Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter”. Research on Biomedical Engineering, 36(2), 107-119, 2020.
  • [15] Sonali SS, Singh AK, Ghrera S, Elhoseny M. “An approach for de-noising and contrast enhancement of retinal fundus image using clahe”. Optics and Laser Technology, 110:87–98, 2019.
  • [16] Toptaş B, Hanbay D. “Retinal blood vessel segmentation using pixel-based feature vector”. Biomedical Signal Processing and Control, 70:103053, 2021.
  • [17] Uysal E, Güraksin G. “Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks”. Multimed Tools Appl, 80, 2021.
  • [18] Tan M, Le Q. “Efficientnet: Rethinking model scaling for convolutional neural networks”. In International conference on machine learning (pp. 6105-6114). PMLR.,2019.
  • [19] Gupta N, Garg H, Agarwal R. “A robust framework for glaucoma detection using CLAHE and EfficientNet”. The Visual Computer, 1-14,2021.
  • [20] Toptaş B, Toptaş M, Hanbay D. “Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space”. Journal of Digital Imaging, 1-18,2022.
  • [21] Azzopardi G, Strisciuglio N, Vento M, Petkov N. “Trainable COSFIRE filters for vessel delineation with application to retinal images”. Medical image analysis, 19(1), 46-57,2015.
  • [22] Elangovan P, Nath MK. “Glaucoma assessment from color fundus images using convolutional neural network” International Journal of Imaging Systems and Technology, 31(2), 955-971,2021.
  • [23] Serte S., Serener A. “A generalized deep learning model for glaucoma detection”. In 2019 3rd International symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1-5). IEEE,2019.
  • [24] Christopher M., Nakahara K., Bowd C., Proudfoot J. A., Belghith A., Goldbaum M. H., ... Zangwill L. M. “Effects of study population, labeling and training on glaucoma detection using deep learning algorithms”. Translational vision science & technology, 9(2), 27-27,2020.
  • [25] Almeida-Galárraga D, Benavides-MK., Insuasti-Cruz E, Lovato-Villacís N, Suárez-Jaramillo V, Tene-Hurtado D, Tirado-Espín A, Villalba-Meneses GF. “Glaucoma detection through digital processing from fundus images using MATLAB”. In 2021 Second International Conference on Information Systems and Software Technologies (ICI2ST) (pp. 39-45). IEEE, 2021.
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Buket Toptaş 0000-0003-2556-8199

Davut Hanbay 0000-0003-2271-7865

Early Pub Date December 31, 1899
Publication Date December 31, 2022
Submission Date September 13, 2022
Acceptance Date November 2, 2022
Published in Issue Year 2022 Volume: 11 Issue: 4

Cite

IEEE B. Toptaş and D. Hanbay, “The Separation of glaucoma and non-glaucoma fundus images using EfficientNet-B0”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 11, no. 4, pp. 1084–1092, 2022, doi: 10.17798/bitlisfen.1174512.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS