@inproceedings{scholars9100,
           pages = {489--492},
         journal = {Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Classification of SD-OCT images using a Deep learning approach},
            year = {2017},
             doi = {10.1109/ICSIPA.2017.8120661},
            note = {cited By 63; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915},
          author = {Awais, M. and Muller, H. and Tang, T. B. and Meriaudeau, F.},
            isbn = {9781509055593},
        keywords = {Classification (of information); Image classification; Image processing; Network layers; Neural networks; Optical data processing; Optical tomography, Convolutional neural network; Diabetic patient; Different layers; Feature matrices; Learning approach; Macular edema; Sensitivity and specificity; Visual graphics, Deep learning},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041381718&doi=10.1109\%2fICSIPA.2017.8120661&partnerID=40&md5=65f272544d0e3bb460b1ead8b20dbb7b},
        abstract = {Diabetic Macular Edema (DME) is one of the many eye diseases that is commonly found in diabetic patients. If it is left untreated it may cause vision loss. This paper focuses on classification of abnormal and normal OCT (Optical Coherence Tomography) image volumes using a pre-Trained CNN (Convolutional Neural Network). Using VGG16 (Visual Geometry Group), features are extracted at different layers of the network, e.g. before fully connected layer and after each fully connected layer. On the basis of these features classification was performed using different classifiers and results are higher than recently published work on the same dataset with an accuracy of 87.5, with sensitivity and specificity being 93.5 and 81 respectively. {\^A}{\copyright} 2017 IEEE.}
}