This document presents a proposal for a system to detect diabetic retinopathy using deep learning. It summarizes that diabetic retinopathy is a serious eye disease caused by high blood sugar that can lead to blindness. The proposed system would classify retina images into five stages of diabetic retinopathy severity using a convolutional neural network model. This early detection approach could help prevent vision loss by identifying the disease earlier. The document outlines the existing diagnosis methods, proposed system, hardware and software requirements, and potential applications in an android app or integrated with fundus cameras.

1. on
Diabetic Retinopathy Detection
using deep learning
Major project presentation
Under the guidance of
V.Prathima
By:
G.Bhavana[19911A05E1]
M.Sreeja [19911A0591]
R.Rishitha[19911A05A8]
K.Srinithya[19911A05E7]
Project Coordinators
M.Vijaya/A.Swarna

2. Abstract
Diabetic retinopathy is a serious eye disease which is caused due to high blood
sugar and it eventually leads to complete blindness. Light sensing tissues of
retina will be affected due to this disease. The blood vessels in the eyes will be
blocked which leads to leakage of fluid in eye. This condition occurs to people
having diabetics for more than a period of twenty years. Here we are proposing
an artificial based system which can predict the possibility of occurrence of
disease using retina images. This can help in detecting diabetic retinopathy at
early stage and possibly avoid the risk factor of the disease.

3. introduction
 Diabetic retinopathy is a serious eye disease which is caused due to high blood sugar
and it eventually leads to complete blindness. Light sensing tissues of retina will be
affected due to this disease.
 The possible cure to this disease is using detection at early stage. If it is detected earlier
we can prevent from blindness. The treatments and technologies present in today's world
is time consuming.
 The proposed system will detect the occurrence of the disease and also classify the
stages of disease into five categories and produce an output accordingly. This can help
in detecting diabetic retinopathy at early stage and possibly avoid the risk factor of the
disease.

4. EXISTING SYSTEM
 The current Diagnosis techniques are listed below: 1. Visual acuity test:
Uses an eye chart to measure how well a person sees at various distances
(i.e., visual activity).
 Pupil dilation: The eye care professional places drops into the eye to dilate
the pupil. This allows the patient to see more of the retina and look for
signs of diabetic retinopathy afterwards. After the examination, vision
may remain blurred for certain time.
 Fundus Flourescein Angiography (FFA): This is an imaging technique
which relies on the circulation of fluorescein dye to show staining,
leakage, or non-perfusion of the retinal and choroidal vasculature

5. PROPOSING SYSTEM
 In the proposed system, we are building a model for detection of Diabetic Retinopathy
.This model which will be trained, classifies the disease into five different stages.
 The stages are no Diabetics Retinopathy, mild Diabetics Retinopathy, moderate
Diabetics Retinopathy, severe Diabetics Retinopathy, proliferative Diabetics
Retinopathy.
 This model will be created based on Convolutional Neural Network, which extracts
diagnostic features using an algorithm which is trained to classify images across labels.

6. REQUIREMENTS
 Hardware Requirements:
 System : Intel core i5
 Hard Disk : 500 GB.
 Mouse : Logitech
 RAM : 8 GB
 Software Requirements:
 Operating system : Windows 10 / Linux
 Coding Language : Python.
 Toolbox : Anaconda Navigator
 Framework : Flask Webapp
 Database : MySQL
 Dataset : Diabetic Retinopathy ( Kaggle )

7. SCOPE OF THE PROJECT
The model which will be trained using advanced AI Image Processing Algorithms
would be used in the below ways.
 Android Application: An android application can be built which can take color
fundus photography as the input and output as one of the 5 classes which enables
the Physician to understand the stage of the disease and provide the course of the
treatment.
 Fundus Camera: The proposed model can be directly included in the Fundus
Camera where the captured Image can be sent as an input to the model and the
probability of the presence of the disease and the stage can be diagnosed.

8. .
THANK YOU