Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR Spectroscopic Imaging data

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 classification layer that outputs class predictions for
the given brain voxel.
Results: Our CNN method separated glioma grades 3 and 4 and identified Alzheimer’s
disease patients using MRSI and complementary MRI data with high MCC score (Area Under
the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness
over other popular methods as Partial Least Squares or Support Vector Machines. Also, our
method automatically identified the spectral regions most important in the diagnosis process
and we show that these are in good agreement with existing biomarkers from the literature.
Conclusion: Shallow CNNs models integrating image and spectral features improved
quantitative and exploration and diagnosis of brain diseases for research and clinical purposes.