In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer's disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods.

In Vivo Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) can now be used to elucidate and investigate major nerve pathways in the brain. Nerve pathways are constructed by a) calculating a continuous diffusion tensor field from the discrete, noisy, measured DT-MRI data and then b) solving an equation describing the evolution of a fiber tract, in which the local direction vector of the trajectory is identified with the direction of maximum apparent diffusivity. This approach has been validated previously using synthesized, noisy DT-MRI data. Presently, it is possible to reconstruct large white matter structures in the brain, such as the corpus callosum and the pyramidal tracts. Several problems, however, still affect the method's reliability. Its accuracy degrades where the fiber-tract directional distribution is non-uniform, and background noise in diffusion weighted MRIs can cause computed trajectories to jump to different tracts. Nonetheless, this method can provide quantitative information with which to visualize and study connectivity and continuity of neural pathways in the central and peripheral nervous systems in vivo, and holds promise for elucidating architectural features in other fibrous tissues and ordered media.