<![CDATA[Diabetic Retinopathy (DR) is a progressive eye condition that can lead to blindness, particularly affecting individuals with diabetes. It is commonly diagnosed through the examination of digital retinal images, with fundus photography being recognized as a reliable method for identifying abnormalities in the retina of diabetic patients. However, manual diagnosis based on these images is time-consuming and labor-intensive, necessitating the development of automated systems to enhance both accuracy and efficiency. Recent advancements in machine learning, particularly image classification systems, provide a promising avenue for streamlining the diagnostic process. This study aims to classify DR using Convolutional Neural Networks (CNN), explicitly employing the InceptionV3 architecture to optimize performance. This research also explores the impact of different preprocessing and data augmentation techniques on classification accuracy, focusing on the APTOS 2019 Blindness Detection dataset. Data preprocessing and augmentation are crucial steps in deep learning to enhance model generalization and mitigate overfitting. The study uses preprocessing and data augmentation to train the InceptionV3 model. Results indicate that the model achieves 86.5% accuracy on training data and 82.73% accuracy on test data, significantly improving performance compared to models trained without data augmentation. Additionally, the findings demonstrate that the absence of data augmentation leads to overfitting, as evidenced by performance graphs that show a marked decline in test accuracy relative to training accuracy. This research highlights the importance of tailored preprocessing and augmentation techniques in improving CNN models' robustness and predictive capability for DR detection. ]]>
Copyrights © 2025
