Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2020 Oct 20;10:17857. doi: 10.1038/s41598-020-74920-1

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

PMC Copyright notice

Multi-Spectral Neural Network (MSNN) Classifier Diagram. Normalized Diffusion Weighted Images (DWI) b-Value (0, 50, 100, 150, 200, 400, 600) serve as inputs to the model, where nodal weights are applied to the hidden and output layers to yield output voxels which are classified as normal, tumor, or background once the model is trained. In all cases, training sets represented a random NF1 subject from the population which has been manually segmented by a trained expert. In the input layer N-, L-, and B-prefixes represent the Normal, Lesion, and Background inputs, respectively, while R-N in each set refers to Registered-Normalized images.