Computer aided diagnosis systems based on brain imaging are an important tool
to assist in the diagnosis of Parkinson’s disease. The ultimate goal would be detec-
tion by automatic recognizing of patterns that characterize the disease. In recent
times Convolutional Neural Networks (CNN) have proved to be amazingly useful
for that task. The drawback, however, is that 3D brain images contains a huge
amount of information that leads to complex CNN architectures. When these
architectures become too complex, classification performances often degrades be-
cause the limitations of the training algorithm and overfitting. Thus, this paper
proposes the use of isosurfaces as a way to reduce such amount of data while
keeping the most relevant information. These isosurfaces are then used to im-
plement a classification system which uses two of the most well-known CNN
architectures to classify DaTScan images with an average
accuracy of 95.1% and AUC=97%, obtaining comparable (slightly better) values
to those obtained for most of the recently proposed systems. It can be concluded
therefore that the computation of isosurfaces reduces the complexity of the inputs
significantly, resulting in high classification accuracies with reduced computa-
tional burden.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
