Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT
- 78 Downloads
In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM).
In total, 529 3D-OCT images from the Tsukazaki hospital ophthalmology database (184 non-ERM subjects and 205 ERM patients) were assessed; 80% of the images were divided for training, and 20% for test as follows: 423 training (non-ERM 245, ERM 178) and 106 test (non-ERM 59, ERM 47) images. Using the 423 training images, a model was created with deep convolutional neural network and SVM, and the test data were evaluated.
The DL model’s sensitivity was 97.6% [95% confidence interval (CI), 87.7–99.9%] and specificity was 98.0% (95% CI, 89.7–99.9%), and the area under the curve (AUC) was 0.993 (95% CI, 0.993–0.994). In contrast, the SVM model’s sensitivity was 97.6% (95% CI, 87.7–99.9%), specificity was 94.2% (95% CI, 84.0–98.7%), and AUC was 0.988 (95% CI, 0.987–0.988).
DL model is better than SVM model in detecting ERM by using 3D-OCT images.
KeywordsDeep learning Support vector machine Epiretinal membrane Optical coherence tomography
I would like to thank M. Miki for creating the figures.
Compliance with ethical standards
Conflict of interest
All authors certify that hey have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Tsukazaki hospital) and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 17.Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410CrossRefPubMedGoogle Scholar
- 19.Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of NIPS, pp 1097–1105Google Scholar
- 21.Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315–323Google Scholar
- 22.Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. JMLR 15(1):1929–1958Google Scholar
- 24.Nesterov Y (1983) A method for unconstrained convex minimization problem with the rate of convergence O (1/k^ 2). Doklady AN USSR 269:543–547Google Scholar
- 25.Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
- 27.Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, CVPR 2009Google Scholar
- 29.Liu B, Liu Y, Zhou K Image classification for dogs and cats. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
- 30.Ohsugi H, Tabuchi H, Enno H, Ishitobi N (2017) Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment. Sci Rep 7(1):9425. https://doi.org/10.1038/s41598-017-09891-x CrossRefPubMedPubMedCentralGoogle Scholar