Enhancing The Precision Detection and Grading of Diabetic Retinopathy through Digital Retinal Imaging Using 3D Convolutional Neural Networks

Autho  Allwine; Mutiara S  Simanjuntak; Wahyu Aji  Pulungan

doi:10.52436/1.jutif.2025.6.3.4387

Authors

Autho Allwine Computer Science, Universitas Lampung, Indonesia
Mutiara S Simanjuntak Department of Electrical and Computer Engineering, National Kaohsiung University Of Science and Tecnology, Jiangong Campus. No 415 Jiangong Rd., Sanmin Dist., Kaohsiung City 807, Taiwan
Wahyu Aji Pulungan Computer Science, Universitas Lampung, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.3.4387

Keywords:

3D Convolutional Neural Networks (3D-CNNs), Binary Classification, Data Augmentation, Deep Learning, Diabetic Retinopathy (DR), Diagnostic Precision, Multiclass Classification, Ophthalmology

Abstract

Diabetic retinopathy (DR) is a pressing global health issue that affects the retina and is closely linked to diabetes, leading to vision impairment and blindness, particularly in adults. With the rising incidence of diabetes, the need for efficient and accurate DR screening is critical for early intervention and improved patient outcomes. Automated screening solutions can streamline this process, allowing healthcare professionals to focus more on patient care.In this study, we harnessed advanced deep learning techniques, specifically 3D convolutional neural networks (3D-CNNs), to classify DR into binary categories (presence or absence) and five multiclass categories: mild, moderate, no DR, proliferative DR, and severe DR. Our goal was to enhance diagnostic Precision in ophthalmology. To optimize our models, We embraced two methods transformative data augmentation: random shifting and random weak Gaussian blurring, empowering our model to reach new heights,as well as their combination. Our results showed that, for binary classification, the combined augmentation achieved significant success, The multiclass model was trained without any data augmentation excelled in Precision. These findings highlight the importance of large, high-quality research datas in deep learning algorithms. By leveraging advanced methodologies and robust data, we can transform diabetic retinopathy screening, promoting earlier detection and better treatment outcomes for those affected.

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