DCNN For Cataract Disease Detection Based on Model Parallelism

Section: Research Paper
Issue
Vol. 29 No. 2 (2024): Volume 29 Issue 2
Published
Sep 1, 2024
Pages
111-118

Abstract

The retina is susceptible to numerous diseases, and cataracts are most prevalent, especially in developing nations. Cataracts are recognized as one of the most impactful diseases affecting the retina, given their propensity to develop asymptomatically and potentially lead to blindness or impaired vision among the elderly. Timely detection of cataracts and appropriate intervention is pivotal in mitigating disease progression and reducing instances of blindness attributable to this condition. This study provides a deep learning system based on parallel architectures, that utilized a proposed deep convolutional neural network (DCNN), to detect and diagnose cataract disease accurately. ODIR dataset was used for training and validating the proposed model, which achieved 97.7% accuracy for cataract detection, with an inference time of no more than 0.06 sec.

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Authors

Mamoon A Thanoon

Department of Computer and Information Engineering, College of Electronics Engineering, Ninevah University, Mosul, Iraq

ORCID
shefa A. Dawwd

Department of Computer Engineering, College of Engineering, University of Mosul, Iraq

ORCID

Identifiers

https://doi.org/10.33899/arej.2024.147115.1338
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How to Cite

[1]
M. A Thanoon, مأمون, shefa A. Dawwd, and شفاء, “DCNN For Cataract Disease Detection Based on Model Parallelism”, AREJ, vol. 29, no. 2, pp. 111–118, Sep. 2024.
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