Abstract
Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection 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 contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because 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 implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, 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 computational burden.
Highlights
Parkinson’s Disease (PD) is a progressive and chronic neurodegenerative disorder of the central nervous system that affects movement
We describe a classifier based on the well-known Convolutional Neural Networks (CNN) LeNet-5 and AlexNet where the image features used to train them are isosurfaces computed from the regions of interest
Classification performances increase slightly with the threshold, until this is 0.7. This is explained because the greater the threshold the less the volume captured by the isosurfaces
Summary
Parkinson’s Disease (PD) is a progressive and chronic neurodegenerative disorder of the central nervous system that affects movement. PD increases its occurrence with age and, currently, has a prevalence between 1 and 3% in the population over 65 years of age, becoming the second most common neurodegenerative disorder after the Alzheimer’s disease. The origin of the disease has been not determined yet but it is related to the loss of dopaminergic neurons, which causes reduced quantities of dopamine transporters in the striatum (Simuni and Rajesh, 2009). Dopaminergic neurons produce dopamine, a neurotransmitter, in the substantia nigra and and it is transported to the striatum, composed by caudate and putamen, through the nigrostriatal pathway. To date there is no cure for PD but early diagnosis allows limiting the rate of progression by applying effective management and may help to develop new therapeutic methods. Diagnosis of PD is usually based on clinical examinations that analyze different motor symptoms such as tremor, bradykinesia, rigidity and postural instability (Eckert et al, 2007), along with the response
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