Diagnosis of Alzheimer's Disease Based on Accelerated Mirror Descent Optimization and a Three-Dimensional Aggregated Residual Network.

Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic.

Alzheimer's disease diagnosis via multimodal feature fusion.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. Early diagnosis of AD plays a vital role in slowing down the progress of AD because there is no effective drug to treat the disease. Some deep learning models have recently been presented for AD diagnosis and have more satisfactory performance than classic machine learning methods. Nevertheless, most of the existing computer-aided diagnostic models used neuroimaging features for diagnosis, ignoring patients' clinical and biological information. This makes the AD diagnosis inaccurate. In this study, we propose a novel multimodal feature transformation and fusion model for AD diagnosis. The feature transformation aims to avoid the difference in feature dimensions between different modal data and further mine the significant features for AD diagnosis. A geometric algebra-based feature extension method is proposed to obtain different levels of high-dimensional features from patients' clinical and personal biological data. Then, an influence degree-based feature filtration algorithm is proposed to filtrate those features that have no apparent guiding significance for AD diagnosis. Finally, an ANN (Artificial Neural Network)-based framework is designed to fuse transformed features with neuroimaging features extracted by CNN (Convolutional Neural Network) for AD diagnosis. The more in-depth feature mining of patients' clinical information and biological information can significantly improve the performance of computer-aided AD diagnosis. The experiments are obtained on the ADNI dataset. Our proposed model can converge faster and achieves 96.2% accuracy in AD diagnostic task and 87.4% accuracy in MCI (Mild Cognitive Impairment) diagnostic task. Compared with other methods, our proposed approach has an excellent performance in AD diagnosis and surpasses SOTA (state-of-the-art) methods. Therefore, our model can provide more reasonable suggestions for clinicians to diagnose and treat disease.

Diagnosis of Alzheimer's Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network.

Alzheimer's disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person's ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer's disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer's disease.

1,2-Dibromoethane on Cu(100): bonding structure and transformation to C2H4.

Temperature-programmed reaction/desorption, mass spectrometry, reflection-absorption infrared spectroscopy, x-ray photoelectron spectroscopy, and density functional theory calculations have been employed to explore the reaction and bonding structure of 1,2-C(2)H(4)Br(2) on Cu(100). Both the trans and gauche conformers are found to dissociate by breaking the C-Br bonds on clean Cu(100) at 115 K, forming C(2)H(4) and Br atoms. Theoretical investigations for the possible paths of 1,2-C(2)H(4)Br(2) → C(2)H(4) + 2Br on Cu(100) suggest that the barriers of the trans and gauche molecules are in the ranges of 0-4.2 and 0-6.5 kcal/mol, respectively. The C-Br scission temperature of C(2)H(4)Br(2) is much lower than that (~170 K) of C(2)H(5)Br on Cu(100). Adsorbed Br atoms can decrease the dissociation rate of the 1,2-C(2)H(4)Br(2) molecules impinging the surface. The 1,2-C(2)H(4)Br(2) molecules adsorbed in the first monolayer are structurally distorted. Both the trans and gauche molecules exist in the second monolayer, but with no preferential adsorption orientation. However, the trans molecule is the predominant species in the third or higher layer formed at 115 K. The layer structure is not thermally stable. Upon heating the surface to 150 K, the orientation of the trans 1,2-C(2)H(4)Br(2) molecules in the layer changes, leading to the rotation of the BrCCBr skeletal plane toward the surface normal on average and the considerable growth of the CH(2) scissoring peak. On oxygen-precovered Cu(100), decomposition of 1,2-C(2)H(4)Br(2) to form C(2)H(4) is hampered and no oxygenated hydrocarbons are formed. The presence of the oxygen atoms also increases the adsorption energy of the second-layer molecules.