King Abdullah International Medical Research Center (KAIMRC)’s big ocular images data set

Almazroa, Ahmed; Alawad, Mohammed; Aljouie, Abdulrhman; Alkanhal, Norah; Almatar, Hessa; Algoblan, Mohammed

doi:10.1117/12.2612529

Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications 2022

DOI: 10.1117/12.2612529

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King Abdullah International Medical Research Center (KAIMRC)’s big ocular images data set

Ahmed Almazroa

Mohammed Alawad

Abdulrhman Aljouie

et al.

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Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Almansour

Alawad²,

Aljouie³

et al. 2022

PLoS ONE

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Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. The model was developed based on localization for the region of interest (ROI) using a mask region-based convolutional neural networks R-CNN and a classification network for the presence of PPA using CNN deep learning algorithms. A total of 2,472 images, obtained from five public sources and one Saudi-based resource (King Abdullah International Medical Research Center in Riyadh, Saudi Arabia), were used to train and test the model. First the images from public sources were analyzed, followed by those from local sources, and finally, images from both sources were analyzed together. In testing the classification model, the area under the curve’s (AUC) scores of 0.83, 0.89, and 0.87 were obtained for the local, public, and combined sets, respectively. The developed model will assist in diagnosing glaucoma in screening programs; however, more research is needed on segmenting the PPA boundaries for more detailed PPA detection, which can be combined with optic disc and cup boundaries to calculate the cup-to-disc ratio.

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Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Almansour

Alawad²,

Aljouie³

et al. 2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

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King Abdullah International Medical Research Center (KAIMRC)’s big ocular images data set

Cited by 1 publication

References 10 publications

Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Peripapillary atrophy classification using CNN deep learning for glaucoma screening

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