Automated retinal disorders classification: leveraging digital image enhancement techniques and deep learning on OCT images

Mahdi Kargar Nigjeh; Mahsa Kargar Nigjeh; Scott E. Umbaugh

doi:10.1117/12.3028346

30 September 2024 Automated retinal disorders classification: leveraging digital image enhancement techniques and deep learning on OCT images

Mahdi Kargar Nigjeh, Mahsa Kargar Nigjeh, Scott E. Umbaugh

Author Affiliations +

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131370K (2024) https://doi.org/10.1117/12.3028346
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Retinal Optical Coherence Tomography (OCT) plays a pivotal role in diagnosing ocular disorders by providing detailed imaging of retinal layers. However, the analysis process remains time-consuming, posing a challenge to its widespread use.

This study investigates the integration of Artificial Intelligence (AI) to streamline the analysis of OCT images. Employing Deep Learning (DL) models—VGG16, ResNet18, DenseNet—transfer learning, and data augmentation, the research aims to enhance OCT images, optimize disease recognition, and accurately classify CNV (Choroidal Neovascularization), DME (Diabetic Macular Edema), DRUSEN, and NORMAL pathologies.

The dataset undergoes preprocessing, resizing, and enhancement to refine the images. The DenseNet model achieved the highest test accuracy of 92.41% after 25 epochs, demonstrating its potential in efficiently diagnosing ocular pathologies through OCT images.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mahdi Kargar Nigjeh, Mahsa Kargar Nigjeh, and Scott E. Umbaugh "Automated retinal disorders classification: leveraging digital image enhancement techniques and deep learning on OCT images", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131370K (30 September 2024); https://doi.org/10.1117/12.3028346

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