Neural network platform for recognizing a sequence of cell images

Alexandra Psarrou; Hilary Buxton

doi:10.1117/12.139983

16 September 1992 Neural network platform for recognizing a sequence of cell images

Alexandra Psarrou, Hilary Buxton

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139983
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

The aim of the research introduced in this paper is to develop a unified neural network platform to model the behavior of cancerous cells. Neural networks are used to both extract features from the cell images and control the process for recognizing whether a sequence of cell images match the locomotive and social behavior of a cancerous cell and its probability to metastasize. The problem first tackled is the extraction of cell features from the images as, for example, the center of the cell. This paper gives an overview of the application and presents the results drawn from two neural network architectures, an `all connected' and a`locally connected' network, used for the extraction of cell centroid areas from images. Both networks are implemented on a distributed array of processors (DAP) and trained using the backpropagation learning algorithm.

Citation Download Citation

Alexandra Psarrou and Hilary Buxton "Neural network platform for recognizing a sequence of cell images", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139983

ACCESS THE FULL ARTICLE

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