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This paper deals with the automated detection of \gls{dme} on \gls{oct} volumes.
Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan.
Features such as \gls{hog} and \gls{lbp} are extracted and combined to create a set of different feature vectors which are fed to a linear-\gls{svm} Classifier.
Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.
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\section{Discussion and Conclusion}\label{sec:dis-con}

We presented an automatic classification framework for \gls{sdoct} volumes in order to indentify \gls{dme} volumes versus normal Volumes.
In this regard, we investigated a generic pipeline including preprocessing, feature extraction and feature reduction and feature combination.
The best results were obtained for \gls{hog} feature vectors.
We plan to extend our work with a \gls{bow} approach as well as we intend to study other classifiers (\gls{rbf}-\gls{svm}, \gls{rf}) in a near future.
Also, we are currently working on the extension of the dataset so as to make it more challengeable and open to the community.

\section{Acknowledgment}
The authors would like to acknowledge the financial support of the PHC Merlion from France and Singapore.
The authors would like to acknowledge the Regional Burgundy Council which partially financially supported the current project under the PARI scheme 2.



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