• ISSN: 2349-6002
  • UGC Approved Journal No 47859
Author Login IJIRT Partner Network (IPN) Login

AUTOMATED DIABETIC RETINOPATHY DETECTION USING CNN ALGORITHM

  • Unique Paper ID: 161696
  • Volume: 10
  • Issue: 5
  • PageNo: 380-383
  • Abstract:
  • Diabetic Retinopathy (DR) is quite common, among a segment of the population who have diabetes. Diabetic retinopathy is a condition that damages the tissues of diabetic individuals and in severe cases, it leads to permanent blindness if not diagnosed properly for a long time. If diagnosed early, consequences can be minimized. In previous work, the detection of DR is done by Machine learning and image processing techniques. In our proposed research work we employ a pre-trained machine learning algorithm based on Convolutional Neural Networks (CNN) to expedite the diagnosis process by analyzing images Based on disease severity, captured images are classified into five categories based on disease severity. As CNN is a fully connected neural network a Deep Learning algorithm, it needs little pre-processing than other classification methods. In our work, we use a recently developed and efficient model named EfficientNet-B5 Efficient net B5 in conjunction with threshold is implemented to increase the performance of our work. The effectiveness of our method in classifying DR cases. Our work has the potential to reduce the likelihood of blindness, for individuals affected by DR.
email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 5
  • PageNo: 380-383

AUTOMATED DIABETIC RETINOPATHY DETECTION USING CNN ALGORITHM

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry