Long Short-Term Memory Neural Networks for Identifying Type 1 Diabetes Patients with Functional Magnetic Resonance Imaging

摘要

The neuronal activation in the human brain is one of the most complex systems known nowadays that can be measured through functional magnetic resonance imaging (fMRI). Modeling this phenomenon could help in better understanding diseases with an impact on brain. Type 1 diabetes is a disease associated with the metabolism of energy, that has been associated to cognitive disorders. Here, we propose to classify Type 1 diabetic fMRIs during a working memory test using Long Short-Term Memory (LSTM) recurrent artificial neural networks due to its ability to model complex time series. We compared 20 different LSTM architectures on our database using mean and standard deviations of accuracy, specificity and F1 score. Our best result was obtained with a bidirectional LSTM obtaining a mean accuracy of 0.87, mean specificity of 0.89 and mean F1 score of 0.86. Our results have paved the way for doing similar models for other diseases and larger databases.