Diabetic Retinopathy using Thermal Images and Convolutional Neural Network (CNN)

Authors

  • Aarti Gaikwad Student, Dept. Of Computer Engineering, KJCOE PUNE, Maharashtra, India
  • Lokesh Bharate Student, Dept. Of Computer Engineering, KJCOE PUNE, Maharashtra, India
  • Sidhant Meru Student, Dept. Of Computer Engineering, KJCOE PUNE, Maharashtra, India
  • Chetana Chhatre -

Keywords:

Diabetic Retinopathy, Infrared Thermography, CNN

Abstract

Diabetic Retinopathy is one of the serious problem around the world. That can make significant
debilitation the eyes, including a lasting loss of vision. Early discovery of eye maladies builds the endurance rate by
effective treatment. The proposed approach is to investigate AI system to distinguish DR utilizing Thermography
pictures of an eye and to present the impact of warm variety of variation from the norm in the eye structure as a
finding imaging methodologies which are valuable for ophthalmologists to do the clinical determination. Warm
pictures are pre-handled and dependent on surface highlights from dark pictures, factual highlights from RGB and
HSI pictures are extricated and arranged utilizing classifier with different blend of highlights. After that we utilize the
pictures to our CNN classifier model and recognize the Diabetic Retinopathy.

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Published

2020-04-25

How to Cite

Aarti Gaikwad, Lokesh Bharate, Sidhant Meru, & Chetana Chhatre. (2020). Diabetic Retinopathy using Thermal Images and Convolutional Neural Network (CNN). International Journal of Advance Research in Engineering, Science & Technology, 7(4), 18–22. Retrieved from https://ijarest.org/index.php/ijarest/article/view/1982

Issue

Vol. 7 No. 4 (2020): Volume: 07, Issue: 04 - April, 2020.

Section

Articles