ABSTRACT

• ´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.

EXISTING SYSTEM

• ´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.

DISADVANTAGES

• ´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.

PROPOSED SYSTEM

• ´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.

PROJECT DEMO VIDEO

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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