SMARTPHONE BASED SYSTEM FOR DIABETIC RETINOPATHY DETECTION USING ARTIFICIAL NEURAL NETWORK

Project Title

SMARTPHONE BASED SYSTEM FOR DIABETIC RETINOPATHY DETECTION USING ARTIFICIAL NEURAL NETWORK

Project Area of Specialization Artificial IntelligenceProject Summary

Early diagnosis of diabetic retinopathy for the treatment of the disease has been failing 

to reach diabetic people living in rural areas. The shortage of trained ophthalmologists, 

limited availability of healthcare centers, and expensiveness of 

diagnostic equipment are among the reasons.

In this project we are going to develop a  Deep Learning Model of Diabetic Retinopathy

 with an  android application. First the Dataset of  Diabetic Retinopathy is trained  after 

that through mobile phone we attach a handheld ophthalmoscope and take high resolution

 Fundus images and then Deep learning model then detect and classify the

 Diabetic Retinopathy and grade the stages.

Project Objectives

• To Develop an  artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) by fundus images using a smartphone.

• Identify and classify different Stages of Diabetic Retinopathy

• Train and test a model in TENSORFLOW (ML/DL).

• Acquire images taken from a 20D Retina lens attached to a smartphone for testing and training of models. 

• Develop a user-friendly Android App.

• Minimize chances of False-Diagnosis.

• Reduce the amount of time and cost required for diseases diagnosed using a Microscope.

• To provide people living in remote areas a quick and easy method for Detection of Diabetic Retinopathy without the need of an ophthalmologist.( eye specialist )

Project Implementation Method

• Artificial Neural Network

• Image Processing

• For training a deep learning model, a dataset of fundus images from Kaggle website will  

be used. The dataset contains fundus images labeled into five stages of DR.

•  Image Pre-processing For model training, 2000 images were selected from each stage of DR.

 In order to exploit high performance from a deep learning model and smaller training dataset;

 image preprocessing will be  performed. after that large datasets will be used.

• The fundus images are then cropped to remove unnecessary black background pixels. 

• The local average was subtracted from the cropped fundus image.

The designed deep learning model will be imported to the smartphone.

fig. 2 Project Implementation Flowchart

Benefits of the Project

• Early Detection of Diabetic Retinopathy. 

• This mobile eye examination system is easily accessible to anyone living in the city or 

in a village.

• Cost-efficiency: Existing methods include costs incurred for expensive sophisticated fundus

 cameras and operating technicians. Our system, on the other hand, uses a low-cost portable 

handheld 20D retina lens and a smart-phone.

• Portability: Our compact system is easy-to-deploy on field locations that are hundreds of 

miles away from specialists. This is unlike present-day heavy equipment.

• Ease of operation: Presently, ophthalmologists rely extensively on specially trained 

personnel to capture images using the fundus cameras. Our system simplifies the task of 

image collection independent of the operator.

• Decision-making capability: In contrast to other systems which require interpretation of 

images by an ophthalmologist, our system provides a first-hand assessment of conditions to

general practitioners and emergency room physicians

Technical Details of Final Deliverable

The final deliverable will be an Android App which will acquire Fundus images from a 20D lens attached to a smartphone. The App will detect and grade the stages of Diabetic Retinopathy.

fig. 3 20D retina lens mounted on the backside of the mobile phone camera.

fig. 4 Fundus Image captured with a smartphone with a 20D lens mounted.
 

Final Deliverable of the Project HW/SW integrated systemCore Industry HealthOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development GoalsRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	25000
20D Retina Lens	Equipment	1	25000	25000