Dr. Horea Demea Profile

Dr. Horea Demea

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 27 May 2022 Poster + Presentation + Paper

Macular edema degeneration classification on OCT and fundus images with portable platform based on artificial intelligence methods

Loredana Buzura, Monica Loredana Budileanu, Radu Papara, Horea Demea, Ramona Galatus

Proceedings Volume 12144, 121440L (2022) https://doi.org/10.1117/12.2617520

KEYWORDS: Embedded systems, Computer aided design, Machine learning, Optical coherence tomography, Retina, Eye, Image classification, Diagnostics

Read Abstract +

Age-related macular degeneration (AMD) is the leading cause of permanent vision loss and visual impairment in people over 60. Early detection of the disease is essential to prevent the evolution of the disease into an advanced stage. An eye care specialist has to perform a dilated eye exam, fundoscopy, a visual acuity test, and fundus photography to determine if a patient has macular degeneration and the stage of the disease. Most of the equipment used nowadays in eye care clinics is equipped in one system with both fundus camera and OCT technology that provides more comprehensive clinical evaluations. In most countries the healthcare system suffers from a low doctor to patient ratio; due to it, diagnosis can become time-consuming and error-prone. To minimize this downfall, a computer-aided diagnosis (CAD) strategy is proposed using machine learning techniques to predict the presence of age-related macular degeneration using both OCT and fundus images. The computer-aided diagnosis (CAD) is using a portable device, Jetson TX2 board, a powerful AI computer device, to predict the presence of an abnormality in the retina. A dataset composed of three categories: normal retina, dry AMD, and wet AMD from both OCT and fundus images have been used to evaluate the performance of different neuronal networks. Cost reduction and system portability are implemented with the proposed system for point of care in ophthalmology applications.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years