Derin Öğrenme ve Adaptif Histogram Eşitleme Kullanarak Retinal Hastalıkların Fundus Görüntülerinden Tespiti
Öz
Günümüzde, katarakt, diyabetik retinopati, glokom, maküler ödem, miyop ve astigmat gibi çeşitli göz hastalıkları sıklıkla görülmektedir. Bu hastalıklardan katarakt, diyabetik retinopati ve glokom, teşhis ve tedavi edilmedikleri durumlarda, bulanık görmeye, görme kaybına ve hatta körlüğe neden olmaktadır. Uzman ve donanım eksikliği, donanımsal problemler ve uzmanlarca verilen hatalı kararlar gibi çeşitli nedenlerle, teşhis aşamasında sorunlarla karşılaşılmaktadır. Bu nedenlerden dolayı, Bilgisayar Destekli Teşhis sistemlerine ihtiyaç duyulmaktadır. Son zamanlarda, derin öğrenme algoritmalarıyla gerçekleştirilen çalışmalarda, başarılı sonuçlar elde edilmiştir. Bu başarılı sonuçlar, derin öğrenmenin, göz hastalıklarının teşhisinde kullanılabileceğini göstermektedir. Bu çalışmada, çeşitli CNN modelleri kullanılarak, fundus görüntüleri üzerinden, katarakt, diyabetik retinopati ve glokom gibi göz hastalıklarının sınıflandırılması gerçekleştirilmiştir. Fundus görüntülerinde, Kontrast Sınırlı Adaptif Histogram Eşitleme yöntemi kullanılmıştır. Deneysel sonuçlar, VGG16 modelinin, bu üç model arasında en başarılı model olduğunu ve Kontrast Sınırlı Adaptif Histogram Eşitleme yönteminin de performansı arttırdığını göstermektedir.
Anahtar Kelimeler
Derin öğrenme Diyabetik retinopati Katarakt Glokom Görüntü sınıflandırma
Kaynakça
- Gadekallu, T. R. and Khare, N., et al. 2020. Deep neural networks to predict diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing. (2020), 1-14.
- Wong, T. Y. and Sun, J., et al. 2018. Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology. 125 (2018), 1608-1622.
- Ting, D. S. W. and Pasquale, L. R., et al. 2019. Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology. 103 (2019), 167-175.
- Yau, J. W. and Rogers, S. L., et al. 2012. Global prevalence and major risk factors of diabetic retinopathy. Diabetes care. 35 (2012), 556-564.
- Tan, G. S. and Cheung, N., et al. 2017. Diabetic macular oedema. The lancet Diabetes & endocrinology. 5 (2017), 143-155.
- Beede, E. and Baylor, E., et al. 2020. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Proceedings of the 2020 CHI conference on human factors in computing systems, 1-12.
- Mansour, R. F. 2018. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomedical engineering letters. 8 (2018), 41-57.
- Gök, A. E., and Taşdemir, Ş. 2023. Santral Seröz Koryoretinopati Hastalığının Derin Öğrenme ile Teşhisi: Derleme Makalesi, Online, 112-122.
- Li, T. and Gao, Y., et al. 2019. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences. 501 (2019), 511-522.
- Li, J. and Xu, X., et al. 2018. Automatic cataracts diagnosis by image-based interpretability. 2018 IEEE international conference on systems, man, and cybernetics (SMC), 3964-3969.
- Xu, X. and Zhang, L., et al. 2019. A hybrid global-local representation CNN model for automatic cataracts grading. IEEE journal of biomedical and health informatics. 24 (2019), 556-567.
- Asbell, P. A. and Dualan, I., et al. 2005. Age-related cataracts. The Lancet. 365 (2005), 599-609.
- Gao, X. and Lin, S. Wong, T. Y. 2015. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering. 62 (2015), 2693-2701.
- Kanski, J. J., and Bowling, B. 2011. Clinical ophthalmology: a systematic approach.
- Luo, Y. and Chen, K., et al. 2020. Dehaze of cataractous retinal images using an unpaired generative adversarial network. IEEE Journal of Biomedical and Health Informatics. 24 (2020), 3374-3383.
- Pratap, T., and Kokil, P. 2019. Computer-aided diagnosis of cataracts using deep transfer learning. Biomedical Signal Processing and Control. 53 (2019), 101533.
- 2022. Eye Diseases Classification. Kaggle, (2022),
- Chen, X. and Xu, Y., et al. 2015. Glaucoma detection based on deep convolutional neural network. 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 715-718.
- Chai, Y. and Liu, H. Xu, J. 2018. Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowledge-Based Systems. 161 (2018), 147-156.
- Schacknow, P. N., and Samples, J. R. 2010. The glaucoma book: a practical, evidence-based approach to patient care.
- Haleem, M. S. and Han, L., et al. 2013. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Computerized medical imaging and graphics. 37 (2013), 581-596.
- Tymchenko, B. and Marchenko, P. Spodarets, D. 2020. Deep learning approach to diabetic retinopathy detection. arXiv preprint arXiv:2003.02261. (2020),
- Alrajjou, S. and Boahen, E. K., et al. 2022. An enhanced interpretable deep learning approach for diabetic retinopathy detection. 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 127-135.
- Ayala, A. and Ortiz Figueroa, T., et al. 2021. Diabetic retinopathy improved detection using deep learning. Applied Sciences. 11 (2021), 11970.
- Acar, E. and Türk, Ö., et al. 2021. Employing deep learning architectures for image-based automatic cataracts diagnosis. Turkish Journal of Electrical Engineering and Computer Sciences. 29 (2021), 2649-2662.
- Kalyani, B. and Hemavathi, U., et al. 2023. Smart cataracts detection system with bidirectional LSTM. Soft Computing. (2023), 1-9.
- Junayed, M. S. and Islam, M. B., et al. 2021. CataractNet: An automated cataracts detection system using deep learning for fundus images. IEEE Access. 9 (2021), 128799-128808.
- Li, F. and Yan, L., et al. 2020. Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs. Graefe's Archive for Clinical and Experimental Ophthalmology. 258 (2020), 851-867.
- Bajwa, M. N. and Malik, M. I., et al. 2019. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC medical informatics and decision making. 19 (2019), 1-16.
- Kim, M. and Han, J. C., et al. 2019. Medinoid: computer-aided diagnosis and localization of glaucoma using deep learning. Applied Sciences. 9 (2019), 3064.
- Zhang, H. and Niu, K., et al. 2019. Automatic cataracts grading methods based on deep learning. Computer methods and programs in biomedicine. 182 (2019), 104978.
- Salvi, M. and Acharya, U. R., et al. 2021. The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine. 128 (2021), 104129.
- Goodfellow, I. and Bengio, Y. Courville, A. 2016. Deep learning.
- Lundervold, A. S., and Lundervold, A. 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 29 (2019), 102-127.
- Alzubaidi, L. and Zhang, J., et al. 2021. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data. 8 (2021), 1-74.
- Öztürk, B., and Bilgiç, G. 2022. Yenilenebilir Enerji Tahmini İçin Derin Öğrenme Yaklaşımları. Teknobilim-2022: Enerji Krizi ve Yenilenebilir Enerji. (2022), 111.
- LeCun, Y. and Bengio, Y. Hinton, G. 2015. Deep learning. nature. 521 (2015), 436-444.
- Kübra, U., and Taşdemir, Ş. 2021. Detection and classification of leucocyte types in histological blood tissue images using deep learning approach. Avrupa Bilim ve Teknoloji Dergisi. (2021), 130-137.
- Howard, A. G. and Zhu, M., et al. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. (2017),
- Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. (2014),
- Sugata, T., and Yang, C. 2017. Leaf App: Leaf recognition with deep convolutional neural networks. IOP Conference Series: Materials Science and Engineering, 012004.
- Huang, G. and Liu, Z., et al. 2017. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708.
- Bülbül, M.A. 2023Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis/prediction. The Journal of Supercomputing. (2023), 1-21.
- Jose, D. 2023. Classification of EYE Diseases Using Multi-Model CNN. 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), 1-6.
- Şener, B., and Sümer, E. Classification of Eye Disease from Retinal Images Using Deep Learning.
Detection of Retinal Diseases from Fundus Images Using Deep Learning and Adaptive Histogram Equality
Öz
Recently, various eye diseases such as cataracts, diabetic retinopathy, glaucoma, macular edema, myopia, and astigmatism have been seen frequently. Cataracts, diabetic retinopathy, and glaucoma cause blurred vision, loss of vision, and blindness in cases where they are left untreated and undiagnosed. Lack of experts and equipment, hardware problems, and erroneous decisions made by experts cause problems in the diagnosis process. Because of these reasons, computer-aided diagnosis systems that can diagnose accurately are required. Deep learning algorithms performed well in the field of health, recently. These results show that deep learning algorithms can be used in the diagnosis of eye diseases. In this study, various CNN models were used for classifying eye diseases such as cataracts, diabetic retinopathy, and glaucoma from fundus images. In the image preprocessing stage, the Contrast Limited Adaptive Histogram Equalization method was used. Experimental results demonstrate that VGG16 was the most successful model among the evaluated models in this study and the Contrast Limited Adaptive Histogram Equalization method increased the performance.
Anahtar Kelimeler
Deep Learning Diabetic Retinopathy Cataract Glaucoma Image Classification
Kaynakça
- Gadekallu, T. R. and Khare, N., et al. 2020. Deep neural networks to predict diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing. (2020), 1-14.
- Wong, T. Y. and Sun, J., et al. 2018. Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology. 125 (2018), 1608-1622.
- Ting, D. S. W. and Pasquale, L. R., et al. 2019. Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology. 103 (2019), 167-175.
- Yau, J. W. and Rogers, S. L., et al. 2012. Global prevalence and major risk factors of diabetic retinopathy. Diabetes care. 35 (2012), 556-564.
- Tan, G. S. and Cheung, N., et al. 2017. Diabetic macular oedema. The lancet Diabetes & endocrinology. 5 (2017), 143-155.
- Beede, E. and Baylor, E., et al. 2020. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Proceedings of the 2020 CHI conference on human factors in computing systems, 1-12.
- Mansour, R. F. 2018. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomedical engineering letters. 8 (2018), 41-57.
- Gök, A. E., and Taşdemir, Ş. 2023. Santral Seröz Koryoretinopati Hastalığının Derin Öğrenme ile Teşhisi: Derleme Makalesi, Online, 112-122.
- Li, T. and Gao, Y., et al. 2019. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences. 501 (2019), 511-522.
- Li, J. and Xu, X., et al. 2018. Automatic cataracts diagnosis by image-based interpretability. 2018 IEEE international conference on systems, man, and cybernetics (SMC), 3964-3969.
- Xu, X. and Zhang, L., et al. 2019. A hybrid global-local representation CNN model for automatic cataracts grading. IEEE journal of biomedical and health informatics. 24 (2019), 556-567.
- Asbell, P. A. and Dualan, I., et al. 2005. Age-related cataracts. The Lancet. 365 (2005), 599-609.
- Gao, X. and Lin, S. Wong, T. Y. 2015. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering. 62 (2015), 2693-2701.
- Kanski, J. J., and Bowling, B. 2011. Clinical ophthalmology: a systematic approach.
- Luo, Y. and Chen, K., et al. 2020. Dehaze of cataractous retinal images using an unpaired generative adversarial network. IEEE Journal of Biomedical and Health Informatics. 24 (2020), 3374-3383.
- Pratap, T., and Kokil, P. 2019. Computer-aided diagnosis of cataracts using deep transfer learning. Biomedical Signal Processing and Control. 53 (2019), 101533.
- 2022. Eye Diseases Classification. Kaggle, (2022),
- Chen, X. and Xu, Y., et al. 2015. Glaucoma detection based on deep convolutional neural network. 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 715-718.
- Chai, Y. and Liu, H. Xu, J. 2018. Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowledge-Based Systems. 161 (2018), 147-156.
- Schacknow, P. N., and Samples, J. R. 2010. The glaucoma book: a practical, evidence-based approach to patient care.
- Haleem, M. S. and Han, L., et al. 2013. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Computerized medical imaging and graphics. 37 (2013), 581-596.
- Tymchenko, B. and Marchenko, P. Spodarets, D. 2020. Deep learning approach to diabetic retinopathy detection. arXiv preprint arXiv:2003.02261. (2020),
- Alrajjou, S. and Boahen, E. K., et al. 2022. An enhanced interpretable deep learning approach for diabetic retinopathy detection. 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 127-135.
- Ayala, A. and Ortiz Figueroa, T., et al. 2021. Diabetic retinopathy improved detection using deep learning. Applied Sciences. 11 (2021), 11970.
- Acar, E. and Türk, Ö., et al. 2021. Employing deep learning architectures for image-based automatic cataracts diagnosis. Turkish Journal of Electrical Engineering and Computer Sciences. 29 (2021), 2649-2662.
- Kalyani, B. and Hemavathi, U., et al. 2023. Smart cataracts detection system with bidirectional LSTM. Soft Computing. (2023), 1-9.
- Junayed, M. S. and Islam, M. B., et al. 2021. CataractNet: An automated cataracts detection system using deep learning for fundus images. IEEE Access. 9 (2021), 128799-128808.
- Li, F. and Yan, L., et al. 2020. Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs. Graefe's Archive for Clinical and Experimental Ophthalmology. 258 (2020), 851-867.
- Bajwa, M. N. and Malik, M. I., et al. 2019. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC medical informatics and decision making. 19 (2019), 1-16.
- Kim, M. and Han, J. C., et al. 2019. Medinoid: computer-aided diagnosis and localization of glaucoma using deep learning. Applied Sciences. 9 (2019), 3064.
- Zhang, H. and Niu, K., et al. 2019. Automatic cataracts grading methods based on deep learning. Computer methods and programs in biomedicine. 182 (2019), 104978.
- Salvi, M. and Acharya, U. R., et al. 2021. The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine. 128 (2021), 104129.
- Goodfellow, I. and Bengio, Y. Courville, A. 2016. Deep learning.
- Lundervold, A. S., and Lundervold, A. 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 29 (2019), 102-127.
- Alzubaidi, L. and Zhang, J., et al. 2021. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data. 8 (2021), 1-74.
- Öztürk, B., and Bilgiç, G. 2022. Yenilenebilir Enerji Tahmini İçin Derin Öğrenme Yaklaşımları. Teknobilim-2022: Enerji Krizi ve Yenilenebilir Enerji. (2022), 111.
- LeCun, Y. and Bengio, Y. Hinton, G. 2015. Deep learning. nature. 521 (2015), 436-444.
- Kübra, U., and Taşdemir, Ş. 2021. Detection and classification of leucocyte types in histological blood tissue images using deep learning approach. Avrupa Bilim ve Teknoloji Dergisi. (2021), 130-137.
- Howard, A. G. and Zhu, M., et al. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. (2017),
- Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. (2014),
- Sugata, T., and Yang, C. 2017. Leaf App: Leaf recognition with deep convolutional neural networks. IOP Conference Series: Materials Science and Engineering, 012004.
- Huang, G. and Liu, Z., et al. 2017. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708.
- Bülbül, M.A. 2023Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis/prediction. The Journal of Supercomputing. (2023), 1-21.
- Jose, D. 2023. Classification of EYE Diseases Using Multi-Model CNN. 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), 1-6.
- Şener, B., and Sümer, E. Classification of Eye Disease from Retinal Images Using Deep Learning.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Görüntü İşleme, Derin Öğrenme |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 30 Nisan 2024 |
| Yayımlanma Tarihi | 30 Nisan 2024 |
| Gönderilme Tarihi | 29 Ocak 2024 |
| Kabul Tarihi | 1 Nisan 2024 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 40 Sayı: 1 |
Kaynak Göster
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