@inproceedings{scholars12929,
           pages = {1863--1866},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
            year = {2020},
           title = {Performance Evaluation of Convolutions and Atrous Convolutions in Deep Networks for Retinal Disease Segmentation on Optical Coherence Tomography Volumes},
             doi = {10.1109/EMBC44109.2020.9175639},
            note = {cited By 3; Conference of 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference Date: 20 July 2020 Through 24 July 2020; Conference Code:162693},
          volume = {2020-J},
            issn = {1557170X},
          author = {Alsaih, K. and Yusoff, M. Z. and Tang, T. B. and Faye, I. and Meriaudeau, F.},
            isbn = {9781728119908},
        keywords = {Artificial intelligence; Convolution; Deep neural networks; Deterioration; Ophthalmology; Optical tomography; Semantics; Tomography, Age-related macular degeneration; Artificial intelligence techniques; Competitive performance; Human expertise; Human levels; Older People; Retinal disease; Semantic segmentation, Diagnosis},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091044354&doi=10.1109\%2fEMBC44109.2020.9175639&partnerID=40&md5=878a3ca1a3e61ab31a5cbb3c2a56b9c0},
        abstract = {The deterioration of the retina center could be the main reason for vision loss. Older people usually ranging from 50 years and above are exposed to age-related macular degeneration (AMD) disease that strikes the retina. The lack of human expertise to interpret the complexity in diagnosing diseases leads to the importance of developing an accurate method to detect and localize the targeted infection. Approaching the performance of ophthalmologists is the consistent main challenge in retinal disease segmentation. Artificial intelligence techniques have shown enormous achievement in various tasks in computer vision. This paper depicts an automated end-to-end deep neural network for retinal disease segmentation on optical coherence tomography (OCT) scans. The work proposed in this study shows the performance difference between convolution operations and atrous convolution operations. Three deep semantic segmentation architectures, namely U-net, Segnet, and Deeplabv3+, have been considered to evaluate the performance of varying convolution operations. Empirical outcomes show a competitive performance to the human level, with an average dice score of 0.73 for retinal diseases. {\^A}{\copyright} 2020 IEEE.}
}