A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks

Abstract

Background. As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease. Method. In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture. In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM. Result. We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is 89.95 % in fivefold cross-validation. Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively. Conclusion. It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness.

[Retracted] The Value of Nuclear Magnetic Resonance in Liver Nodular Lesions

“…Some studies have reported that MRI and fMRI can clearly show the changes in the shape and size of the harness in the state of liver disease, which is of great reference value for evaluating the diagnosis of chronic liver disease and the functional reserve of the liver. The results of this study found that the sensitivity of fMRI in diagnosing benign nodular liver lesions was 83.33%, the specificity was 85.29%, the accuracy was 84.09%, and the Kappa value was 0.672, suggesting that fMRI has a certain reference value in the differential diagnosis of benign and malignant liver sarcoidosis [ 18 ]. CEUS detected 43 lesions and fMRI detected 45 lesions, and the sensitivity was higher than that of CEUS, it can be seen that fMRI can make up for the deficiency of CEUS.…”