Diabetic Retinopathy Classification using SVM Classifier

Vishakha Vinod Chaudhari, Prof. Pankaj Salunkhe

ABSTRACT The common cause of blindness is diabetes nowadays in working age populations. Many patients eyesight can be affected due to diabetes. People do not know the cause of blindness due to diabetes and are also unaware about different diseases caused by diabetes. Patients can suffer from dieses like cataract, glaucoma, bleeding of blood vessels, etc. Any damage caused to the blood vessels in the eye due to diabetes is called as diabetic retinopathy. Diabetic retinopathy is a micro vascular complication which can cause several changes in the retina. There are many changes which can occur like change in the diameter of blood vessels, growth of new blood vessels, micro aneurysms, hemorrhage, exudates, etc. These changes must be detected at an early stage. Diabetic macular edema (DME) is a complication of DR. DME is defined as swelling of the eye retina in diabetic patients due to leakage of fluid within the central macula from the dilated small blood vessels. LASER therapy is one of the common therapies for patients but this technology uses manual examination of scanned results and the results can differ. So to overcome this problem a special program is needed to analyze the different part of the eye. This paper proposes a special detection technique for analyzing the images of the eye. It includes preprocessing of the eye image then the image will be resized and converted into a grey scale image. Then with the help of feature extraction various parameters will be operated and images having different features will be stored to data base. Based on which the new images will be analyzed and compared so as to detect the exact problem the patient is suffering. This paper also discusses diagnosis of DME using features based on color, wavelet decomposition and automatic lesion segmentation. Keywords: Diabetics, SVM, Automatic diagnosis, Lesion, DR

Title:	Diabetic Retinopathy Classification using SVM Classifier
Author Name:	Vishakha Vinod Chaudhari, Prof. Pankaj Salunkhe
Abstract:	ABSTRACT The common cause of blindness is diabetes nowadays in working age populations. Many patients eyesight can be affected due to diabetes. People do not know the cause of blindness due to diabetes and are also unaware about different diseases caused by diabetes. Patients can suffer from dieses like cataract, glaucoma, bleeding of blood vessels, etc. Any damage caused to the blood vessels in the eye due to diabetes is called as diabetic retinopathy. Diabetic retinopathy is a micro vascular complication which can cause several changes in the retina. There are many changes which can occur like change in the diameter of blood vessels, growth of new blood vessels, micro aneurysms, hemorrhage, exudates, etc. These changes must be detected at an early stage. Diabetic macular edema (DME) is a complication of DR. DME is defined as swelling of the eye retina in diabetic patients due to leakage of fluid within the central macula from the dilated small blood vessels. LASER therapy is one of the common therapies for patients but this technology uses manual examination of scanned results and the results can differ. So to overcome this problem a special program is needed to analyze the different part of the eye. This paper proposes a special detection technique for analyzing the images of the eye. It includes preprocessing of the eye image then the image will be resized and converted into a grey scale image. Then with the help of feature extraction various parameters will be operated and images having different features will be stored to data base. Based on which the new images will be analyzed and compared so as to detect the exact problem the patient is suffering. This paper also discusses diagnosis of DME using features based on color, wavelet decomposition and automatic lesion segmentation. Keywords: Diabetics, SVM, Automatic diagnosis, Lesion, DR
Cite this article:	Vishakha Vinod Chaudhari, Prof. Pankaj Salunkhe , " Diabetic Retinopathy Classification using SVM Classifier " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 6, Issue 7, July 2017 , pp. 007-011 , ISSN 2319 - 4847.
Full Text [PDF] Home