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Bölgesel-Evrişimsel Sinir Ağları ile Retina Görüntülerindeki Lezyonların Tespiti

Yıl 2020, Cilt: 7 Sayı: 1, 34 - 46, 28.06.2020

Süleyman Uzun

https://doi.org/10.35193/bseufbd.681195

Öz

Şeker hastalığı gözün yapısını etkileyen ve görme kayıplarına sebep olan bir hastalıktır. Göz yapısında çok çeşitli lezyon türlerinin oluşmasına neden olur. Retina görüntüleri üzerinde bulunan bu lezyonlar farklı hastalıkların belirtisi olmaktadır. Bu hastalıkların başında en bilineni diyabetik retinopati rahatsızlığıdır. Bu rahatsızlığı erken teşhis ve tedavisinde lezyonların tespiti oldukça önemli olmaktadır. Yapılan çalışmada, retina görüntüleri üzerinde bulunan lezyonların tespiti için Bölgesel-Evrişimsel Sinir Ağları temelli bilgisayar destekli tespit sistemi önerilmiştir. Önerilen bu sistemle göz hastalıkları alanda çalışan uzmanların teşhis ve tedavisine destek olması hedeflenmiştir. Çalışmada kullanılan retina görüntüleri STARE, DIARETDB0 ve DIARETDB1 veri tabanlarından elde edilmiştir. Kullanılan veri tabanlarında bulunan görüntülerin %70’i eğitim ve %30’u test görüntüsü olarak ayrılmıştır. Bölgesel-Evrişimsel Sinir Ağları, eğitim aşamasında çok fazla veriye ihtiyaç duymasından dolayı eğitim görüntülerin etiketlenmesi amacıyla dikdörtgen şeklinde ve tek görüntü üzerinden birden fazla alanın seçilmesine imkan sağlayan bir bölge seçicide tasarlanmıştır. Retina görüntüleri derin öğrenme uygulamalarında sıkça kullanılan cifar-10 ön-eğitimli ağı üzerinde eğitilmiştir. Eğitimler sonunda yapılan test işlemlerinde STARE, DIARETDB0 ve DIARETDB1 veri tabanlarında sırasıyla lezyonu bölgeyi bulma başarımları %91, %98.53 ve %93.01 doğruluk ile başarılı bir şekilde tespit etmiştir.

Anahtar Kelimeler

Retina Görüntüleri Bölgesel-Evrişimsel Sinir Ağları Derin Öğrenme R-CNN

Kaynakça

  • Rocha, A., Carvalho, T., Jelinek, H. F., Goldenstein, S., & Wainer, J. (2012). Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE Transactions on Biomedical Engineering, 59(8), 2244–2253. https://doi.org/10.1109/TBME.2012.
  • Salomão, S. R., Mitsuhiro, M. R. K. H., & Belfort Jr, R. (2009). Visual impairment and blindness: an overview of prevalence and causes in Brazil. Anais Da Academia Brasileira de Ciências, 81(3), 539–549. https://doi.org/10.1590/S0001-37652009000300017.
  • Singh, T. M., Bharali, P., & Bhuyan, C. (2019). Automated detection of diabetic retinopathy. In 2019 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICACCP.2019.8882914.
  • Kasim, Ö. (2018). Detection of lesions on the retina image. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1–4). IEEE. https://doi.org/10.1109/SIU.2018.8404532.
  • Quellec, G., Russell, S. R., & Abràmoff, M. D. (2011). Optimal Filter Framework for Automated, Instantaneous Detection of Lesions in Retinal Images. IEEE Transactions on Medical Imaging, 30(2), 523–533. https://doi.org/10.1109/TMI.2010.2089383.
  • Murugan, R., Albert, A. J., & Nayak, D. K. (2019). An Automatic Localization of Microaneurysms in Retinal Fundus Images. In 6th IEEE International Conference on Smart Structures and Systems ICSSS 2019. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICSSS.2019.8882858.
  • Atila, Ü., Akyol, K., & Sabaz, F. (2020). Retinal Görüntülerde Eksuda Lezyonlarının Tespiti Üzerine Bir Çalışma. Bilişim Teknolojileri Dergisi, 13(1), 27–36. https://doi.org/10.17671/gazibtd.550022
  • Carrera, E. V., Gonzalez, A., & Carrera, R. (2017). Automated detection of diabetic retinopathy using SVM. In 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1–4). IEEE. https://doi.org/10.1109/INTERCON.2017.8079692.
  • Dandıl, E., Turkan, M., Boğa, M., & Çevik, K. K. (2019). Daha Hızlı Bölgesel-Evrişimsel Sinir Ağları ile Sığır Yüzlerinin Tanınması. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 6, 177–189. https://doi.org/10.35193/bseufbd.592099
  • Çevik, K. K., & Dandıl, E. (2019). Classification of Lung Nodules Using Convolutional Neural Networks on CT Images. In 2nd International Turkish World Engineering and Science Congress (pp. 27–35). Retrieved from https://www.researchgate.net/publication/338385647_Classification_of_Lung_Nodules_Using_Convolutional_Neural_Networks_on_CT_Images
  • Ari, A., & Hanbay, D. (2019). Tumor detection in MR images of regional convolutional neural networks. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(3), 1395–1408. https://doi.org/10.17341/gazimmfd.460535.
  • Dandil, E., & Polattimur, R. (2019). Daha hızlı bölgesel evrişimsel sinir ağları ile köpek davranışlarının tanınması ve takibi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(2), 819–834. https://doi.org/10.17341/gazimmfd.541677.
  • Hoover, A. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203–210. https://doi.org/10.1109/42.845178.
  • Hoover, A., & Goldbaum, M. (2003). Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Transactions on Medical Imaging, 22(8), 951–958. https://doi.org/10.1109/TMI.2003.815900
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2016-Decem, pp. 770–778). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.90.
  • Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Uusitalo, H., Kälviäinen, H., Pietilä, J. (2007). DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms. Retrieved from https://www.it.lut.fi/project/imageret/diaretdb0/doc/diaretdb0_techreport_v_1_1.pdf
  • Kauppi Tomi, Kalesnykiene Valentina, Sorri Iiris, Raninen Asta, Voutilainen Raija, Kamarainen Joni, L. L. and U. H. (2009). DiaRetDB1: Diabetic Retinopathy Database and Evaluation Protocol. Retrieved from http://www.it.lut.fi/project/imageret/diaretdb1_v2_1/
  • Nie, X., Duan, M., Ding, H., Hu, B., & Wong, E. K. (2020). Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images. IEEE Access, 8, 9325–9334. https://doi.org/10.1109/ACCESS.2020.2964540
  • Rodin, C. D., de Lima, L. N., de Alcantara Andrade, F. A., Haddad, D. B., Johansen, T. A., & Storvold, R. (2018). Object Classification in Thermal Images using Convolutional Neural Networks for Search and Rescue Missions with Unmanned Aerial Systems. In 2018 International Joint Conference on Neural Networks (IJCNN) (Vol. 2018-July, pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN.2018.848946
  • İnik, Ö., & Ülker, E. (2017). Gaziosmanpaşa Bilimsel Araştırma Dergisi (GBAD) Gaziosmanpasa Journal of Scientific Research Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. GAZİOSMANPAŞBi̇li̇msel AraştirmDergi̇si̇, 6(3), 85–104. Retrieved from http://dergipark.gov.tr/gbad
  • Le, P.-P., Nguyen, V.-T., Guo, S.-M., Tu, C.-T., & Lien, J.-J. J. (2019). Visual-Guided Robot Arm Using Multi-Task Faster. 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2–7. https://doi.org/10.1109/TAAI48200.2019.8959938
  • Fang, F., Li, L., Zhu, H., & Lim, J.-H. (2019). Combining Faster R-CNN and Model-Driven Clustering for Elongated Object Detection. IEEE Transactions on Image Processing, 29, 1–1. https://doi.org/10.1109/tip.2019.2947792
  • He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2020). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 386–397. https://doi.org/10.1109/TPAMI.2018.2844175
  • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587). IEEE. https://doi.org/10.1109/CVPR.2014.81

Detection of Lesions On Retinal Images Using The Regional-Convolutional Neural Networks

Yıl 2020, Cilt: 7 Sayı: 1, 34 - 46, 28.06.2020

Süleyman Uzun

https://doi.org/10.35193/bseufbd.681195

Öz

Diabetes is a disease that affects the structure of the eye and causes vision loss. It causes a wide variety of lesion types in the eye structure. It causes a wide variety of lesion types in the eye structure. These lesions on the retina images are symptoms of different diseases. The most well-known of these diseases is diabetic retinopathy. Detection of lesions is very important in early diagnosis and treatment of this ailment. In the study, a computer-assisted detection system based on Regional-Evolutionary Neural Networks has been proposed for the detection of lesions on the retinal images. With this proposed system, it is aimed to support the diagnosis and treatment of specialists working in the field of eye diseases. Retina images used in the study were obtained from STARE, DIARETDB0 and DIARETDB1 databases. 70% of the images in the databases used are devoted to education and 30% to test images. Regional-Evolutionary Neural Networks are designed in a region selector that allows multiple areas to be selected over a single image in order to tag educational images since they require a lot of data during the training phase. Retina images are trained on the cifar-10 pre-trained network, which is frequently used in deep learning practices. In the test operations performed at the end of the trainings, STARE, DIARETDB0 and DIARETDB1 databases successfully detected the lesion in the database with 91%, 98.53% and 93.01% accuracy, respectively.

Anahtar Kelimeler

Retina images Regional-Convolutional Neural Network Deep Learning R-CNN

Kaynakça

  • Rocha, A., Carvalho, T., Jelinek, H. F., Goldenstein, S., & Wainer, J. (2012). Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE Transactions on Biomedical Engineering, 59(8), 2244–2253. https://doi.org/10.1109/TBME.2012.
  • Salomão, S. R., Mitsuhiro, M. R. K. H., & Belfort Jr, R. (2009). Visual impairment and blindness: an overview of prevalence and causes in Brazil. Anais Da Academia Brasileira de Ciências, 81(3), 539–549. https://doi.org/10.1590/S0001-37652009000300017.
  • Singh, T. M., Bharali, P., & Bhuyan, C. (2019). Automated detection of diabetic retinopathy. In 2019 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICACCP.2019.8882914.
  • Kasim, Ö. (2018). Detection of lesions on the retina image. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1–4). IEEE. https://doi.org/10.1109/SIU.2018.8404532.
  • Quellec, G., Russell, S. R., & Abràmoff, M. D. (2011). Optimal Filter Framework for Automated, Instantaneous Detection of Lesions in Retinal Images. IEEE Transactions on Medical Imaging, 30(2), 523–533. https://doi.org/10.1109/TMI.2010.2089383.
  • Murugan, R., Albert, A. J., & Nayak, D. K. (2019). An Automatic Localization of Microaneurysms in Retinal Fundus Images. In 6th IEEE International Conference on Smart Structures and Systems ICSSS 2019. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICSSS.2019.8882858.
  • Atila, Ü., Akyol, K., & Sabaz, F. (2020). Retinal Görüntülerde Eksuda Lezyonlarının Tespiti Üzerine Bir Çalışma. Bilişim Teknolojileri Dergisi, 13(1), 27–36. https://doi.org/10.17671/gazibtd.550022
  • Carrera, E. V., Gonzalez, A., & Carrera, R. (2017). Automated detection of diabetic retinopathy using SVM. In 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1–4). IEEE. https://doi.org/10.1109/INTERCON.2017.8079692.
  • Dandıl, E., Turkan, M., Boğa, M., & Çevik, K. K. (2019). Daha Hızlı Bölgesel-Evrişimsel Sinir Ağları ile Sığır Yüzlerinin Tanınması. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 6, 177–189. https://doi.org/10.35193/bseufbd.592099
  • Çevik, K. K., & Dandıl, E. (2019). Classification of Lung Nodules Using Convolutional Neural Networks on CT Images. In 2nd International Turkish World Engineering and Science Congress (pp. 27–35). Retrieved from https://www.researchgate.net/publication/338385647_Classification_of_Lung_Nodules_Using_Convolutional_Neural_Networks_on_CT_Images
  • Ari, A., & Hanbay, D. (2019). Tumor detection in MR images of regional convolutional neural networks. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(3), 1395–1408. https://doi.org/10.17341/gazimmfd.460535.
  • Dandil, E., & Polattimur, R. (2019). Daha hızlı bölgesel evrişimsel sinir ağları ile köpek davranışlarının tanınması ve takibi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(2), 819–834. https://doi.org/10.17341/gazimmfd.541677.
  • Hoover, A. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203–210. https://doi.org/10.1109/42.845178.
  • Hoover, A., & Goldbaum, M. (2003). Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Transactions on Medical Imaging, 22(8), 951–958. https://doi.org/10.1109/TMI.2003.815900
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2016-Decem, pp. 770–778). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.90.
  • Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Uusitalo, H., Kälviäinen, H., Pietilä, J. (2007). DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms. Retrieved from https://www.it.lut.fi/project/imageret/diaretdb0/doc/diaretdb0_techreport_v_1_1.pdf
  • Kauppi Tomi, Kalesnykiene Valentina, Sorri Iiris, Raninen Asta, Voutilainen Raija, Kamarainen Joni, L. L. and U. H. (2009). DiaRetDB1: Diabetic Retinopathy Database and Evaluation Protocol. Retrieved from http://www.it.lut.fi/project/imageret/diaretdb1_v2_1/
  • Nie, X., Duan, M., Ding, H., Hu, B., & Wong, E. K. (2020). Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images. IEEE Access, 8, 9325–9334. https://doi.org/10.1109/ACCESS.2020.2964540
  • Rodin, C. D., de Lima, L. N., de Alcantara Andrade, F. A., Haddad, D. B., Johansen, T. A., & Storvold, R. (2018). Object Classification in Thermal Images using Convolutional Neural Networks for Search and Rescue Missions with Unmanned Aerial Systems. In 2018 International Joint Conference on Neural Networks (IJCNN) (Vol. 2018-July, pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN.2018.848946
  • İnik, Ö., & Ülker, E. (2017). Gaziosmanpaşa Bilimsel Araştırma Dergisi (GBAD) Gaziosmanpasa Journal of Scientific Research Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. GAZİOSMANPAŞBi̇li̇msel AraştirmDergi̇si̇, 6(3), 85–104. Retrieved from http://dergipark.gov.tr/gbad
  • Le, P.-P., Nguyen, V.-T., Guo, S.-M., Tu, C.-T., & Lien, J.-J. J. (2019). Visual-Guided Robot Arm Using Multi-Task Faster. 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2–7. https://doi.org/10.1109/TAAI48200.2019.8959938
  • Fang, F., Li, L., Zhu, H., & Lim, J.-H. (2019). Combining Faster R-CNN and Model-Driven Clustering for Elongated Object Detection. IEEE Transactions on Image Processing, 29, 1–1. https://doi.org/10.1109/tip.2019.2947792
  • He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2020). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 386–397. https://doi.org/10.1109/TPAMI.2018.2844175
  • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587). IEEE. https://doi.org/10.1109/CVPR.2014.81
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Süleyman Uzun 0000-0001-8246-6733

Yayımlanma Tarihi 28 Haziran 2020
Gönderilme Tarihi 28 Ocak 2020
Kabul Tarihi 26 Mart 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 1

Kaynak Göster

APA Uzun, S. (2020). Bölgesel-Evrişimsel Sinir Ağları ile Retina Görüntülerindeki Lezyonların Tespiti. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 7(1), 34-46. https://doi.org/10.35193/bseufbd.681195