• ´Diabetic Retinopathy is the most common cause of vision loss among people with diabetes and the leading cause of vision impairment and blindness among working-age adults.
  • ´By using a certain algorithm the retinal image from the user is fed into system.
  • ´The blood vessels are extracted from the image then it is pre-processed by filtering and segmentation process.
  • ´ It is followed by fractional edge reduction which is used for the feature extraction and by using a Faster retinal convolutional neural network algorithm to automate the diagnosis process.
  • ´It improves the resultant accuracy and by this classification technique we can achieve high accuracy.


  • ´The existing system analysis the presence of microaneurysm in fundus image.
  • ´Semantic segmentation divides the image pixels based on their common semantic to identify the feature of microaneurysm.
  • ´Using convolutional neural network algorithms that embeds deep learning as a core component accelerated with GPU which will perform medical image detection.


  • ´The primary issue is the grading of the retinal images by ophthalmologists (retinal specialists) or trained persons, whose numbers are very scarce compared to the load of patients requiring screening.
  • ´Second, some of these patients are based in rural areas and can’t visit an eye care provider.
  • ´Thirdly, as such follow ups are required for years together, the attitude, and/or behavioral aspects negatively impact the patients practice despite knowledge of consequences.
  • ´These issues can be solved with provision of an automated imaging system within easy reach of the patient.


  • ´The input retina image is collected from the user and fed to the system.
  • ´Image pre-processing steps are applied.
  • ´Feature extraction and feature selection is done to examine for further process.
  • ´Faster retinal convolutional neural network algorithm is used to classify the level of retinopathy.
  • ´Preprocessing Before extracting iris features, the iris image is preprocessed to localize the pupillary (inner) and limbic (outer) boundaries.
  • ´ Subsequently, the localized iris is transformed to a rectangular template of constant size to cope up with the Retinal under varying illumination conditions.
  • ´ This is done using Daugman’s Rubber Sheet Model.
  • ´The normalized strip corresponding to each Retina is first enhanced to ensure extraction of more discriminative features.
  • ´ In this project, the enhancement of iris strip comprises of formation of uniformly illuminated Retinal image and contrast enhancement.
  • ´ To consider the effect of non-uniform illumination, the image is subdivided into blocks and mean of each block will act as coarse estimation of illumination for that block.



Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above


Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

  • * 24/7 Support * Ticketing System
  • * Voice Conference
  • * Video On Demand 
  • * Remote Connectivity
  • * Code Customization
  • * Document Customization 
  • * Live Chat Support

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