Optimizing Eye Disease Diagnosis Algorithms Through Parallel Processing
Year: 2025
Type: article
Abstract: The accurate diagnosis of eye diseases is crucial for preventing severe visual impairments. In this research, I personally propose the optimization of eye disease classification algorithms through the use of parallel processing. More precisely, I utilize deep learning techniques, specifically the EfficientNetV2-B0 model, for the classification of fundus images into four categories: cataract, diabetic retinopathy, glaucoma, and normal. I compare the performance of a sequential model against a parallel multi-GPU model to evaluate the benefits of parallel processing. Research results show a significant reduction in training time with minimal impact on classification accuracy, demonstrating the potential of parallelism in optimizing the computational efficiency of eye disease diagnosis systems.
Author: Eraliyev Seitjan Madali o'g'li
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6911929324
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