Authors :
Prof. Jayshree Aher; Araadhya Sharma; Sashank Vemulapalli
Volume/Issue :
Volume 5 - 2020, Issue 6 - June
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/31OzyRH
Abstract :
Diabetic Retinopathy (DR) is an eye disease
that affects people that suffer from diabetes over
prolonged periods of time. If not detected and diagnosed
at the right time, it often leads to weakening of vision
and can even lead to absolute loss of vision. The disease
generally affects people who are aged between 35 to 50
years, but recent cases involving teenagers have also
been reported widely. The process for diagnosing
Diabetic Retinopathy is often difficult since very few
visible symptoms appear in patients until it is too late
for treatment and the point of no return is met. Current
techniques that exist for detecting Diabetic Retinopathy
are extremely time consuming and require a manual
procedure to be carried out by lab technicians which
involves inserting medical tools into the patient’s eye.
The proposed methodology is to utilize the neoteric
branch of computer science i.e. Machine Learning
techniques to assist in identifying and diagnosing the
disease by analysing the images of the eye. As per the
research study, the images will be preprocessed, and
converted to the Gray Scale following which the
extraction of relevant features using appropriate
supervised learning techniques are carried out to obtain
the final trained model.
Keywords :
Diabetic Retinopathy, Diabetic eye disease, Microaneurysms, Exudates, Machine learning, Supervised learning Introduction
Diabetic Retinopathy (DR) is an eye disease
that affects people that suffer from diabetes over
prolonged periods of time. If not detected and diagnosed
at the right time, it often leads to weakening of vision
and can even lead to absolute loss of vision. The disease
generally affects people who are aged between 35 to 50
years, but recent cases involving teenagers have also
been reported widely. The process for diagnosing
Diabetic Retinopathy is often difficult since very few
visible symptoms appear in patients until it is too late
for treatment and the point of no return is met. Current
techniques that exist for detecting Diabetic Retinopathy
are extremely time consuming and require a manual
procedure to be carried out by lab technicians which
involves inserting medical tools into the patient’s eye.
The proposed methodology is to utilize the neoteric
branch of computer science i.e. Machine Learning
techniques to assist in identifying and diagnosing the
disease by analysing the images of the eye. As per the
research study, the images will be preprocessed, and
converted to the Gray Scale following which the
extraction of relevant features using appropriate
supervised learning techniques are carried out to obtain
the final trained model.
Keywords :
Diabetic Retinopathy, Diabetic eye disease, Microaneurysms, Exudates, Machine learning, Supervised learning Introduction