Computer based Automated Detection of Diabetic Retinopathy Stages using Neural Network

Authors

  • Malay Hitendra Patel Student, Computer Engineering Depatment Saraswati College of Engineering, Kharghar, Maharashtra, India
  • Sayali Sharad Manjrekar Student, Computer Engineering Depatment Saraswati College of Engineering, Kharghar, Maharashtra, India
  • Ashish Dnyaneshwar Parulekar Dnyaneshwar Parulekar Student, Computer Engineering Depatment Saraswati College of Engineering, Kharghar, Maharashtra, India
  • Rina Bora Professor, Computer Engineering Depatment Saraswati College of Engineering, Kharghar, Maharashtra, India

Abstract

Diabetic Retinopathy (DR) is a condition where a person suffering from diabetes for more than 10 years and has problem in vision. Due to DR, blood vessels in retina gets damaged. Patient also suffers from a blurred vision, finds difficulty in seeing colors in some cases or even total loss of vision. Retinal surgeries can relieve the symptoms, but efficient way to tackle DR is controlling diabetes and managing early symptoms of DR, which can be done by having eyes checked at least once annually. DR is a leading cause of blindness. The manual process for studying the fundus images is a time consuming approach and needs expertise to do it. Thus to minimize the time and to detect it more accurately, the proposed system uses deep learning algorithms. Convolutional Neural Networks (CNN) is used to train the model using images of fundus. These fundus images include data of five stages of DR. Satisfactory results were obtained using CNN.

Keywords: Deep learning, Convolutional Neural Networks (CNN), Diabetic Retinopathy (DR), technology, digital image processing.

Cite this Article: Malay Hitendra Patel, Sayali Sharad Manjrekar, Ashish Dnyaneshwar Parulekar, Rina Bora. Computer based Automated Detection of Diabetic Retinopathy Stages using Neural Network. Journal of Multimedia Technology & Recent Advancements. 2020; 7(2): 13–17p.

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Published

2020-08-06

Issue

Vol. 7 No. 2 (2020)

Section

Research Articles

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