Detection of Diabetic Retinopathy Using Deep Learning and Raspberry PI

Diabetic Retinopathy (DR) is a leading cause of preventable blindness globally, affecting individuals with diabetes. Early and accurate diagnosis of DR is critical to prevent vision loss, yet traditional screening methods are labor-intensive and susceptible to subjective variability. This paper presents an automated DR detection system utilizing Convolutional Neural Networks (CNNs) implemented in Python. The proposed method leverages deep learning to analyze retinal fundus images, classifying DR into its various stages. The system incorporates advanced pre-processing techniques, data augmentation, and transfer learning using a pre-trained model to address challenges such as dataset imbalance and variability in image quality. Experimental results demonstrate high accuracy and robustness across diverse datasets, validating the system’s effectiveness in both binary and multi-class DR classification tasks. Furthermore, the model’s lightweight architecture facilitates deployment on edge devices, such as Raspberry Pi. The study underscores the potential of deep learning to revolutionize DR screening, offering scalable, cost-effective solutions to combat blindness globally. This work contributes to bridging the gap between technology and healthcare, highlighting the role of artificial intelligence in enhancing diagnostic capabilities.