Parkinsons Disease Detection by using Isosurfaces with Convolutional Neural Networks

Abstract

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.