This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.

1. DETECTING DIABETIC
RETINOPATHY
THROUGH DEEP
LEARNING AND CNN

2. TEAM
Apurv Wagh, Ashlesha Panse, Abdul Atif
Khan

3. WHAT HAPPENS IN DIABETIC RETINOPATHY?

4. PROBLEM STATEMENT
 Diabetic retinopathy (DR) is a leading cause of vision-loss
globally.
 Of an estimated 285 million people with diabetes mellitus
worldwide, approximately one third have signs of DR and of
these, a further one third of DR is vision-threatening DR.
 As it is a leading disease in the west, there needs to be an
solution and way to identify the disease.

5. The overall national rate is 5.4% for the U.S. population age 40 and older, indicating
that nearly 7.7 million older Americans have diabetic retinopathy.

6. APPROACH TO THE PROBLEM
• Deep Learning: Deep learning is a subset of machine learning in Artificial
Intelligence (AI) that has networks which are capable
from data that is unstructured or unlabeled.
• Convolution Neural Networks: A neural network is a series of algorithms
attempts to identify underlying relationships in a set of data by using a
process that mimics the way the human brain operates.
• Tensorflow: TensorFlow is an open source software library for numerical
computation using data flow graphs.

7. PROCESS FLOW

9. HOW ACTUALLY ARE WE DOING IT?
• We propose a CNN approach to diagnosing DR from digital fundus
images and accurately classifying its severity.
• We develop a network with CNN architecture and data
which can identify the intricate features involved in the classification
task and consequently provide a diagnosis automatically and
user input.
• We train this network using a high-end graphics processor unit
on the publicly available Kaggle dataset and demonstrate
results, particularly for a high-level classification task.

10. MODULES
• Home: It lets the user to see all the functionalities the application provides.
• Image upload: This screen will ask the user to take a picture of the patient’s
fundus by enabling the camera of the phone.
• Result: This screen will display the result of the test conducted.
• Doctors’ list: This screen will give a list of the doctors that could help the
patient with the treatment of the treatment.
• Individual doctor: When a particular doctor is selected, all the information
including the location of the clinic and time and contact number will be
provided.

12. REFERENCES
I. “Kaggle Diabetic Retinopathy Detection competition report”, Ben
Graham, August 6, 2015.
II. Kaggle.com for the dataset of Diabetic Retinopathy.
III. “Severity Classification of Fundus Images for Diabetic Retinopathy”,
Jason Su, Stanford Univeristy.
IV. Diagnosing diabetic retinopathy with deep learning, Robert Bogucki,
September 3, 2015.

13. THANK YOU!
We would be happy to
answer your queries, if any!