Thanoon, M., Dawwd, S. (2024). DCNN For Cataract Disease Detection Based on Model Parallelism. , 29(2), 111-118. doi: 10.33899/arej.2024.147115.1338
Mamoon A Thanoon; shefa A. Dawwd. "DCNN For Cataract Disease Detection Based on Model Parallelism". , 29, 2, 2024, 111-118. doi: 10.33899/arej.2024.147115.1338
Thanoon, M., Dawwd, S. (2024). 'DCNN For Cataract Disease Detection Based on Model Parallelism', , 29(2), pp. 111-118. doi: 10.33899/arej.2024.147115.1338
Thanoon, M., Dawwd, S. DCNN For Cataract Disease Detection Based on Model Parallelism. , 2024; 29(2): 111-118. doi: 10.33899/arej.2024.147115.1338

DCNN For Cataract Disease Detection Based on Model Parallelism

Al-Rafidain Engineering Journal (AREJ)
Volume 29, Issue 2, September 2024, Pages 111-118    PDF (800.33 K)
Document Type: Research Paper
DOI: 10.33899/arej.2024.147115.1338
Authors
Mamoon A Thanoon1; shefa A. Dawwd* 2
1Department of Computer and Information Engineering, College of Electronics Engineering, Ninevah University, Mosul, Iraq
2Department of Computer Engineering, College of Engineering, University of Mosul, Iraq
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.
Keywords
Cataract; Deep learning; Inference; Ocular; CNN
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