Utilising Convolutional Neural Networks for the Automated Detection of Diabetic Retinopathy

Diabetic retinopathy has emerged as one of the leading causes of eye diseases among people suffering from long-term diabetes. Indeed, it raises the risk of being blinded without proper detection and treatment. Conventional detection of retinal fundus images by an ophthalmologist is time-consuming and prone to mistakes owing to human intervention. This is particularly a cause for alarm in the wake of the growing incidence of diabetes globally. The need for automated, accurate detection systems for early diagnosis of diabetic retinopathy has never been more relevant. In this work, we develop a deep learning model based on CNN for classifying four stages of diabetic retinopathy, from no DR to proliferative DR. We tested the model in a public dataset to extract features from retinal images and to screen for abnormalities automatically. Our CNN model achieved 98.5% classification and detection accuracy, thus indicating the ability to make a real difference in the early detection and treatment plan, thereby preserving the vision of many diabetic patients. Practical value The present research is very relevant to clinical practice and, therefore, practically useful because of its relevance to healthcare technology. Using accuracy and loss function metrics, the proposed model performs well compared to the latest techniques in DR detection. An approach based on CNNs is expected to ease much of the workload that healthcare professionals bear in diagnosis and improve the precision, potentially resulting in a very high cutback in vision loss in diabetic patients with better patient outcomes. Our study has highlighted the practical utility of the CNN model, which has improved patient outcomes. DR is a serious condition affecting the eyes, which, in case of untimely detection and untreated in diabetic patients, can cause loss of vision. For centuries, the conventional diagnosis for this disease has been through the manual inspection of retinal fundus images taken through a camera by an ophthalmologist. It is the slowest and most error-prone because it relies on human integrity. The rising worldwide current cases of diabetes pose an imperative for the development of more automation and specificity of the detection instruments towards early diagnosis of DR, an aim of this study.