Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients

2017 ◽  
Vol 38 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Rafia Mumtaz ◽  
Muddasser Hussain ◽  
Saba Sarwar ◽  
Komal Khan ◽  
Sadaf Mumtaz ◽  
...  
Keyword(s):  
Image Processing ◽  
Automatic Detection ◽  
Diabetic Patients ◽  
Retinal Diseases ◽  
Retinal Hemorrhages ◽  
Image Processing Techniques ◽  
Processing Techniques

DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases

2021 ◽  
Author(s):  
Akash Tayal ◽  
Jivansha Gupta ◽  
Arun Solanki ◽  
Khyati Bisht ◽  
Anand Nayyar ◽  
...  
Keyword(s):  
Image Processing ◽  
Retinal Diseases ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Detection of Diabetic Retinopathy by Retinal Screening using Image Processing Techniques

2020 ◽  
pp. 447-457
Author(s):  
Shaziya Banu S ◽  
Ravindra S
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Nearest Neighbor ◽  
Extreme Condition ◽  
Diabetic Patients ◽  
K Nearest Neighbor ◽  
Image Processing Techniques ◽  
Primary Driver ◽  
Processing Techniques

<p>Diabetic Retinopathy (DR) is a related malady with diabetes and primary driver of sightlessness in diabetic patients. Epidemiological overview categorizes DR among four significant reasons for sight impedance. DR is a microvascular entanglement in which meager retinal veins may blast, bringing about vision misfortune. In this condition veins in retina swells and may blast in severe extreme condition. Operative medication is timely discovery by steady screenings that is by emphasizing the determination of retinal images using appropriate image processing techniques such as, Preprocessing of retinal image, image segmentation using sobel edge detector, local features extraction like mean, standard deviation, variance, Entropy, histogram values and so on. For classification of retina, system uses K-Nearest Neighbor (KNN) classifier. By adopting this approach, The classification of normal and abnormal images of retina is easy and will reduce the number of reviews for the ophthalmologists. Developing a method to automate functionality of retinal examination helps doctor to identify patient’s condition on disease. So that they can medicate the disease accordingly.</p>

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