Classification of Optical Coherence Tomography Images for the Diagnosis of Retinal Diseases using Deep Learning Methods
Abstract
The retina is light-sensitive neural layer that enables vision and perception of colors. Distortions in the retina can negatively impact people’s quality of life. Such distortions can lead to serious problems, including blindness and permanent damage to the retina. With the advancement of technology in the treatment of retinal diseases, the use of computer-aided diagnosis systems has become increasing common. Early diagnosis and treament can prevent permanent damage to the retina and help patients retain their vision. As technology has progressed, cameras and computer-aided diagnosis systems have been widely adopted. Retinal images obtained using OCT devices enable experts to make more accurate and early diagnoses. In this study, transfer learning methods, specifically InceptionV3,Xception, and the proposed Convolutional Neural Network(CNN) model, were compared for classifying retinal diseases. The Xception network achieved an accuracy of 95.36%, while the Inception network achieved an accuracy of 98.2%. The proposed CNN architecture achieved an accuracy of 97.51%. The proposed architecture has obtained more successful results in the classification of diabetes and normal diseases based on diseases architecture compared to other methods.
Keywords
Optical Coherence Tomography Deep Learning Convolutional Neural Network Transfer Learning Retinal OCT
References
- Kolb, H., Simple anatomy of the retina. 2012.
- Srinivasan, P.P., et al., Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express, 2014. 5(10): p. 3568-77.
- Gulshan, V., et al., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 2016. 316(22): p. 2402-2410.
- Najeeb, S., et al. Classification of Retinal Diseases from OCT scans using Convolutional Neural Networks. in 2018 10th International Conference on Electrical and Computer Engineering (ICECE). 2018.
- A P, S., et al., OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images. Computer Methods and Programs in Biomedicine, 2021. 200: p. 105877.
- Lu, Z., et al., Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network. Biomedical Signal Processing and Control, 2023. 81: p. 104365.
- S.C., E.P.A. Retina. 2023 [cited 2023 16.4.2023]; Available from: https://www.epawi.com/comprehensive-eye-care-milwaukee/retina/.
- Karalezli, A. and A. Kaderli, Tıp Fakültesi Öğrencileri için Göz Hastalıkları. 2021: p. 1-3.
- Kaggle, 2019.
- Chollet, F., Xception: Deep Learning with Depthwise Separable Convolutions. 2017. 1800-1807.
- Szegedy, C., et al. Rethinking the Inception Architecture for Computer Vision. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.
- Szegedy, C., et al. Going deeper with convolutions. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.
Retinal Hastalıkların Teşhisi için Optik Koherans Tomografi Görüntülerinin Derin Öğrenme Metotları ile Sınıflandırılması
Abstract
Retina, görmeyi sağlayan ışığa ve renklere duyarlı ağ tabakasıdır. Retinadaki bozulmalar insanların yaşam kalitesini düşürmektedir. Retinada meydana gelen hasarlar körlüğe varan ciddi sorunlara sebep olabilmekt e ve retinada kalıcı hasarlar meydana gelebilmektedir. Retinal hastalıkların tedavisinde gelişen teknoloji ile birlikte bilgisayarlı tanı sistemlerinin kullanımı oldukça yaygınlaşmıştır. Erken teşhis ve tedavi edilmesi retina da oluşabilecek kalıcı hasarla rı ve hastaların görme yetisini kaybetmesini önlemektedir Teknolojinin ilerlemesiyle birlikte fotoğraf makineleri ve bilgisayarlı tanı sistemleri oldukça yaygın kullanılmaya başlanmıştır. OCT cihazları kullanılarak elde edilen retinal görüntüler uzmanların daha doğru ve erken teşhis koymalarını sağlamaktadır. Bu çalışmada, retinal hastalıkların sınıflandırılması için transfer öğrenme yöntemlerinden InceptionV3, Xception ve önerilen Evrişimsel Sinir Ağı (ESA) modeli karşılaştırılmıştır. Xception ağında %95.36 oranında doğruluk değerine, Inception ağında ise %98.2 oranında doğruluk oranı elde edilmiştir. Önerin ESA mimarisinde % 97.51 oranında doğruluk oranı elde edilmiştir. Önerilen mimari hastalık bazında diyabet ve
normal hastalıkların sınıflandırılmasında diğer yöntemlerden daha başarılı sonuçlar elde etmiştir.
References
- Kolb, H., Simple anatomy of the retina. 2012.
- Srinivasan, P.P., et al., Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express, 2014. 5(10): p. 3568-77.
- Gulshan, V., et al., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 2016. 316(22): p. 2402-2410.
- Najeeb, S., et al. Classification of Retinal Diseases from OCT scans using Convolutional Neural Networks. in 2018 10th International Conference on Electrical and Computer Engineering (ICECE). 2018.
- A P, S., et al., OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images. Computer Methods and Programs in Biomedicine, 2021. 200: p. 105877.
- Lu, Z., et al., Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network. Biomedical Signal Processing and Control, 2023. 81: p. 104365.
- S.C., E.P.A. Retina. 2023 [cited 2023 16.4.2023]; Available from: https://www.epawi.com/comprehensive-eye-care-milwaukee/retina/.
- Karalezli, A. and A. Kaderli, Tıp Fakültesi Öğrencileri için Göz Hastalıkları. 2021: p. 1-3.
- Kaggle, 2019.
- Chollet, F., Xception: Deep Learning with Depthwise Separable Convolutions. 2017. 1800-1807.
- Szegedy, C., et al. Rethinking the Inception Architecture for Computer Vision. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.
- Szegedy, C., et al. Going deeper with convolutions. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 27, 2023 |
| Submission Date | June 7, 2023 |
| Acceptance Date | August 8, 2023 |
| Published in Issue | Year 2023 Volume: 4 Issue: 3 |
Journal of ESTUDAM Information is indexed by Index Copernicus, Google Scholar, ASOS Index and ROAD index.