A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus - Syed, Saba Raoof; M. A., Saleem Durai (Frontiers in Neuroscience, 2023)
Objective Type 2 Diabetes Mellitus (T2DM) is linked to cognitive deterioration and anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to develop an automatic deep learning-based brain atrophy diagnosis model to detect, segment, classify, and predict the survival rate. Methods Two hundred thirty-five MRI images affected with brain atrophy due to prolonged T2DM were acquired. The dataset was divided into training and testing (80:20%; 188, 47, respectively). Pre-processing is done through a novel convolutional median filter, followed by segmentation of atrophy regions, i.e., the brain shrinkage, white and gray matter is done through the proposed TRAU-Net model (Transfer Residual Attention U-Net), classification with the proposed Multinomial Logistic regression with Attention Swin Transformer (MLAST), and prediction of chronological age is determined through Multivariate CoX Regression model (MCR). The classification of Brain Atrophy (BA) types is determined based on the features extracted from the segmented region. Performance measures like confusion matrix, specificity, sensitivity, accuracy, F1-score, and ROC-AUC curve are used to measure classification model performance, whereas, for the segmentation model, pixel accuracy and dice similarity coefficient are applied. Results The pixel accuracy and dice coefficient for segmentation were 98.25 and 96.41, respectively. Brain atrophy multi-class classification achieved overall training accuracy is 0.9632 ± 1.325, 0.9677 ± 1.912, 0.9682 ± 1.715, and 0.9521 ± 1.877 for FA, PA, R-MTA, and L-MTA, respectively. The overall AUC-ROC curve for the classification model is 0.9856. The testing and validation accuracy obtained for the proposed model are 0.9379 and 0.9694, respectively. The prediction model's performance is measured using correlation coefficient (r), coefficient determination r2, and Mean Square Error (MSE) and recorded 0.951, 0.904, and 0.5172, respectively. Conclusion The brain atrophy diagnosis model consists of sub-models to detect, segment, and classify the atrophy regions using novel deep learning and multivariate mathematical models. The proposed model has outperformed the existing models regarding multi-classification and segmentation; therefore, the automated diagnosis model can be deployed in healthcare centers to assist physicians.