Skip to main content

Advanced Search

Projected result for your current search: 0

Search Operators:

You can use operators in your search query to narrow down your results even more. Click on the search operator to see an explanation of how it works.
Finds documents with exactly this word group, in exactly this word order and spelling (e.g. "employer branding").
Finds documents with both search terms (e.g. sales AND bonus).
Finds documents with one or the other search term (e.g. porsche OR volkswagen).
Finds documents with all search terms. Blank space is understood as AND (e.g. man robot production).
Finds documents with no appearance of the word behind NOT (e.g. ford NOT "harrison ford").
Finds documents where the search term is mentioned at least "n" times."n" may be any number (e.g. COUNT(gear)>8).
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Finds documents with the search term in different spellings. "?" allows only one character (e.g. organi?ation).
Special characters are understood as AND (e.g. Miller Bros. & Sons).

Semantic Search

powered by (Link opens in a new window)
loading …
Log in
Top

A Comprehensive Study of Machine Learning Techniques for Diabetic Retinopathy Detection

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Rachna Kumari
Sanjeev Kumar
Sunila Godara
Published in
International Conference on Innovative Computing and Communications
Publisher
Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The chapter delves into the growing prevalence of diabetic retinopathy and the necessity for early detection. It discusses various machine learning techniques, such as Support Vector Machines (SVM), Random Forests, and K-Nearest Neighbors (KNN), that have been employed for automatic detection. The study also explores the advancements in deep learning, particularly Convolutional Neural Networks (CNN), which have shown promising results in detecting diabetic retinopathy. The chapter concludes with a discussion on the challenges and future directions in the field, emphasizing the need for more robust models that can handle issues like low image brightness and layer thinning.
AI Generated

This summary of the content was generated with the help of AI.

Already a customer? Then log in here to gain access.

Customer login

Not a customer yet? Then find out more about our access models now:

Individual Access

Start your personal individual access now. Get instant access to more than 164,000 books and 540 journals – including PDF downloads and new releases.

Starting from 54,00 € per month!    

Get access

Access for Businesses

Utilise Springer Professional in your company and provide your employees with sound specialist knowledge. Request information about corporate access now.

Find out how Springer Professional can uplift your work!

Contact us now

For explorers: Get to know our platforms better with a free registration.

Register for free
previous chapter Machine Learning and Deep Learning-Based Detection and Analysis of COVID-19 in Chest X-Ray Images
next chapter Evolution of WSN into WSN-IoT: A Study on its Architecture and Integration Challenges
Title
A Comprehensive Study of Machine Learning Techniques for Diabetic Retinopathy Detection
Authors
Rachna Kumari
Sanjeev Kumar
Sunila Godara
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
International Conference on Innovative Computing and Communications

Print ISBN: 978-981-19-3678-4

Electronic ISBN: 978-981-19-3679-1

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-3679-1_13
This content is only visible if you are logged in and have the appropriate permissions.