Project House
Diabetic Retinopathy Detection
#Deep LearningReact JS


Diabetic Retinopathy (DR) is a complication caused by diabetes that affects the human eye. It is caused by the mutilation of the blood vessels of the light-sensitive tissue at the back of the human retina. It’s the most recurrent cause of blindness in the working age group of people and is highly likely when diabetes is poorly controlled. Although, methods to detect Diabetic Retinopathy exist, they involve manual examination of the retinal image by an Ophthalmologist. Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early detection and intervention are crucial to prevent irreversible damage to the retina. This project focuses on the development of an accurate and efficient system for the detection of diabetic retinopathy using deep learning techniques. The proposed methodology employs DenseNet121 to automatically extract meaningful features from digital fundus images. These images are acquired through retinal screening, and the deep learning model is trained to classify them into different stages of diabetic retinopathy, ranging from mild to severe.The image is classified into several stages of DR such as normal, mild, moderate, severe, and proliferative diabetic retinopathy. The frontend of the web application is developed using ReactJS, and Backend using Python and Firebase.

You can download abstract from here

Technologies Used

FrontendReact JS
BackendFirebase, Python
Project IDDL007B


  1. User
  • User Authentication: Login and Signup functionalities ensure secure access to the system.
  • Image Upload: Users can upload images of plant leaves for analysis. The system processes these images to detect the presence and type of disease.
  • Disease Classification: The trained model analyzes the uploaded leaf image and classifies them into one of 39 predefined disease categories.
  • Prediction History: Users can view the history of their previous predictions, allowing for tracking and management of plant health over time
  • Remedies and Fertilizers: Based on the detected disease, the system provides users with appropriate remedies and recommended fertilizers to address the issue effectively.