ABSTRACT
Now-a-days adults throughout the world will become blind due to diabetic retinopathy (DR). Prompt identification and appropriate treatments are crucial for averting visual impairment in individuals with diabetes. Automating the identification and diagnosis of diabetic retinopathy from retinal fundus images has demonstrated encouraging outcomes with deep learning approaches. This study provides a thorough examination of recent developments in deep learning-based methodologies for the identification of diabetic retinopathy. The aim of classifying diabetic retinopathy (DR) in retinal images. Our model underwent training using a comprehensive dataset that included images of both diabetic retinopathy cases and normal retinal images. Before training commenced, we performed thorough pre-processing to ensure uniformity in the size and format of the images. By leveraging deep learning algorithms and libraries such as NumPy, Flask, Keras, pandas, and Tensor Flow, our model was trained to accurately classify retinal images into various categories, including different stages of diabetic retinopathy as well as normal retinal images. Upon completing the training phase, our model exhibited robust performance in classifying retinal images, consistently achieving high levels of accuracy and reliability. When tested with a different setoff data, our model always made correct predictions, telling apart diabetic retinopathy cases from normal retinal images accurately. Additionally, we created a simple interface allowing users to upload retinal images effortlessly for classification and view the model's results. This interface facilitates seamless interaction with our system, allowing medical professionals and patients to swiftly assess the status of retinal images and make informed decisions regarding further medical evaluation or treatment. In summary, our project signifies a notable progress in diabetic retinopathy detection, offering a reliable and effective solution for automatically classifying retinal images. Using deep learning technology, we’ve created a powerful tool that can make diagnostic procedures better, improve patient results, and help find and treat diabetic retinopathy early.
