Abstract: Diabetic retinopathy (DR) ranks among the foremost contributors to blindness worldwide, particularly affecting the adult demographic. Detecting DR at an early stage is crucial for preventing vision loss; however, conventional approaches like fundus examinations are often lengthy and reliant on specialized expertise. Recent developments in machine learning, especially the application of deep learning models, provide a highly effective option for classifying diabetic retinopathy through retinal fundus images. This investigation examines the efficacy of geometric data augmentation methods alongside MobileNetV2 for the classification of diabetic retinopathy. Utilizing augmentation techniques like image resizing, zooming, shearing, and flipping enhances the model's ability to generalize. MobileNetV2 is selected for its impressive inference speed and computational efficiency. This analysis evaluates the effectiveness of MobileNetV2 in relation to InceptionV3, emphasizing metrics such as accuracy, precision, sensitivity, and specificity. The findings show that MobileNetV2 attains exceptional performance, achieving an accuracy of 97%. These findings highlight the promise of employing efficient models and augmentation strategies in clinical settings for the early identification of DR. The findings highlight the critical need to incorporate advanced machine learning methods to enhance healthcare results and avert blindness caused by diabetic retinopathy.

Helmi Imaduddin, Adnan Faris Naufal, Fiddin Yusfida A'la and Firmansyah, “Enhancing Diabetic Retinopathy Classification Using Geometric Augmentation and MobileNetV2 on Retinal Fundus Images” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151159

@article{Imaduddin2024,
title = {Enhancing Diabetic Retinopathy Classification Using Geometric Augmentation and MobileNetV2 on Retinal Fundus Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151159},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151159},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Helmi Imaduddin and Adnan Faris Naufal and Fiddin Yusfida A'la and Firmansyah}
}