A hybrid convolutional neural network model for detection of diabetic retinopathy

Musa Alshawabkeh, Mohammad Hashem Ryalat, Osama M. Dorgham, Khalid Alkharabsheh, Mohammad Hjouj Btoush, Mamoun Alazab

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Diabetic retinopathy causes vision loss. Regular eye screening has to be done to provide the appropriate treatment and for vision loss prevention. Globally, patients with DR are increasing, which leads to work pressure on specialists and equipment. Fundus images are a key factor in effective retinal diagnosis. In this paper, a deep-learning approach is proposed to detect DR from retinal images. The proposed approach involves a combination of four effective techniques: image augmentation, contrast limited adaptive histogram equalisation, CNN and transfer learning and ensemble classification. The results show the proposed approach obtained high values of accuracy (93%), precision (95%) and recall (96%), and more stability compared with other approaches.

    Original languageEnglish
    Pages (from-to)179-196
    Number of pages18
    JournalInternational Journal of Computer Applications in Technology
    Volume70
    Issue number3-4
    DOIs
    Publication statusPublished - 2022

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