Citation
American Psychological Association 7th edition (APA 7th)
🇺🇸 English, US
Chan, G. C. Y., Shah, S. A. A., Tang, T. B., Lu, C.-K., Muller, H., & Meriaudeau, F. (2018). Deep Features and Data Reduction for Classification of SD-OCT Images: Application to Diabetic Macular Edema. In 2018 International Conference on Intelligent and Advanced System (ICIAS) (pp. 1–4). 2018 International Conference on Intelligent and Advanced System (ICIAS). IEEE. https://doi.org/10.1109/icias.2018.8540579
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Résumé
Diabetic Macular Edema (DME) is defined as the
accumulation of extracellular fluids in the macular
region of the eye, caused by Diabetic Retinopathy (DR)
that will lead to irreversible vision loss if left untreated.
This paper presents the use of a pre-trained
Convolutional Neural Network (CNN) based model for
the classification of Spectral Domain Optical Coherence
Tomography (SD- OCT) images of Diabetic Macular
Edema (DME) with feature reduction using Principal
Component Analysis (PCA) and Bag of Words (BoW).
The model is trained using SD-OCT dataset retrieved
from the Singapore Eye Research Institute (SERI) and
is evaluated using an 8-fold cross validation at the slide
level and two patient leave out at the volume level. For
the volume level, an accuracy of 96.88% is obtained for
data that was preprocessed.