Efficient diabetic retinopathy detection using deep learning approaches and Raspberry Pi 4 - Ajith Kumar, Silpa; Kumar, James Satheesh; Bharadwaj, Sharath Chandra (Institute of Advanced Engineering and Science, 2025)
Diabetic retinopathy (DR) is a leading cause of vision loss, predominantly affecting individuals aged 25-74 with diabetes mellitus. Timely medical intervention can protect against irreversible blindness in over 90% of cases, emphasizing effectively identifying and treating DR. In the scope of deep learning (DL), the possibility of using them in DR screening has garnered a lot of interest. Specifically, we adopted the densely connected convolutional networks (DenseNet) model because to its capacity to acquire complex features and learn from diverse datasets. Developing the computational model on retinal images labelled with varying phases of DR are obtained from databases such as Messidor and Kaggle. To enhance accessibility and user-friendliness, we integrated the DenseNet model into a Raspberry Pi 4, a compact, affordable and widely accessible computing platform. The proposed approach resulted in an impressive classification accuracy of 88%, demonstrating its proficiency in distinguishing between different phases of DR progression. The study aims to assist in the early detection and diagnosis of the disease, providing a potential resource that could help medical practitioners and ophthalmologists to evaluate the extent of DR in a timely manner.