Efficient 3D Residual Network on MRI Data for Neurodegenerative Disease Classification

In recent times, deep learning methods have been employed to learn anatomical and functional brain changes from high discriminative features extracted from neuroimaging data such as Magnetic Resonance Imaging (MRI) which can enhance the performance in the classification and early diagnosis of neurodegenerative diseases. However, features that exist between brain regions that are farther apart are usually not captured by most state-of-the-art deep learning methods. Thus, an effective and robust model for the extraction of high-dimensional descriptive features especially from brain MRI remains an open challenge. In this paper, we investigate the applicability of an enhanced 3D Residual Network (ResNet) for the extraction of high-dimensional descriptive features for an improved classification of neurodegenerative disease using MRI scans. In particular, we enhanced the ResNet-18 by using a dilated convolutional layer instead of the typical convolution layer to expand the receptive field for effective feature extraction and an attention mechanism in the residual blocks to help focus on the relevant extracted features for improved classification. Our proposed method was evaluated on MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Three MRI scan groups were considered: Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognitive (NC). Meanwhile, a three-binary classification task was developed (AD vs. NC, AD vs. MCI, and NC vs. MCI) to test the efficacy of our proposed model. The accuracy of our proposed model for each binary task is 92.12%, 74.07%, and 87.16%, respectively. We further compared the robustness of our proposed model to two state-of-the-art architectures and our model performed better due to its ability to extract discriminative features from the MRI data relevant for the classification tasks. Thus, revealing the effectiveness of our proposed method on the MRI scans.