Diabetics Recognition Using Retina Images

EasyChair Preprint 8229

Abstract

Medical imaging has become increasingly significant in recent years, with millions of imaging analyses performed each week throughout the world. It is a method for identifying abnormalities or studying diseases by developing a visual representation based on the functioning of human organs or tissues. Diseases based on a few situations in the human eye and various parameters may cause changes in our human body, affecting blood pressure, blood glucose, diabetes, heart disease, blood clots in the brain, and other factors. Diabetes is one of the most common diseases, according to many researchers. Due to changes in eye vision, such as difficulty reading or seeing remote objects, blindness, or other abnormalities in the retina of the eye can occur, affecting diabetes in the human body. Diabetes retinopathy is one of the most prevalent diseases that diabetic patients are diagnosed with. In this research, we use computational techniques such as machine learning methods to predict diabetics and compare our outcomes by identifying a few performance indicators that can help us achieve higher accuracy.

Keyphrases: Imutils, Keras, NumPy, OpenCV, Pillow, Python, TensorFlow, Tkinter, deep learning, idle

@booklet{EasyChair:8229,
  author    = {Ravi Kiran and Soumyalatha Naveen},
  title     = {Diabetics Recognition Using Retina Images},
  howpublished = {EasyChair Preprint 8229},
  year      = {EasyChair, 2022}}