Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR spectroscopic imaging data.

ABSTRACT:

Purpose

To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer's disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.

Methods

A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a cla

SUBMITTER: Acquarelli J 

PROVIDER: S-EPMC9401174 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature