A New Classification Network for Diagnosing Alzheimer’s Disease in class-imbalance MRI datasets

Automatic identification of Alzheimer’s Disease (AD) through magnetic resonance imaging (MRI) data can eectively assist to doctors diagnose and treat Alzheimer’s. Current methods improve the accuracy of AD recognition, but they are insufficient to address the challenge of small interclass and large intraclass dierences. Some studies attempt to embed patch-level structure in neural networks which enhance pathologic details, but the enormous size and time complexity render these methods unfavorable. Furthermore, several self-attention mechanisms fail to provide contextual information to represent discriminative regions, which limits the performance of these classifiers. In addition, the current loss function is adversely aected by outliers of class imbalance and may fall into local optimal values. Therefore, we propose a 3D Residual RepVGG Attention network (ResRepANet) stacked with several lightweight blocks to identify the MRI of brain disease, which can also trade o accuracy and flexibility. Specifically, we propose a Non-local Context Spatial Attention block (NCSA) and embed it in our proposed ResRepANet, which aggregates global contextual information in spatial features to improve semantic relevance in discriminative regions. In addition, in order to reduce the influence of outliers, we propose a Gradient Density Multiple-weighting Mechanism (GDMM) to automatically adjust the weights of each MRI image via a normalizing gradient norm. Experiments are conducted on datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL). Experiments on both datasets show that the accuracy, sensitivity, specificity, and Area Under the Curve are consistently better than for state-of-the-art methods

Alzheimer\u27s Disease Classification Using Distilled Multi-Residual Network

Early human intervention is crucial for diagnosing Alzheimer’s Disease (AD), since AD is irreversible and leads to progressive impairment of memory. In recent years, Convolutional Neural Networks (CNNs) have achieved dramatic breakthroughs in AD diagnosis. However, existing CNNs have difficulties in extracting subtle contextual information because of their structural limitations, i.e., it is difficult to extract discriminative features of several regions, such as hippocampus, parietal, temporal lobe tissues, and so on. In addition, current networks have difficulty in classifying imaging features with imbalanced data categories. Some loss functions can alleviate the above problems to some extent, but they are affected by outliers and trapped in local optimum easily. To address this issue, a Distilled Multi-Residual Network (DMRNet) is proposed for the early diagnosis of AD. The DMRNet consists of three main components: 1) Dense Connection Block, 2) Focus Attention Block, and 3) Multi-scale Fusion Module. The dense features are extracted by the Dense Connection Block while the local abnormal regions are refined by the Focus Attention Block and Multi-scale Fusion Module. Besides, to explore the hidden knowledge between each feature, a dilated classifier with self-distillation is proposed to ensemble several items pertaining to feature knowledge from feature space. Finally, the Remix Balance Sampler (RBS) is proposed to alleviate the influences of outliers. The proposed DMRNet is evaluated on baseline sMRI scans of the ADNI dataset. The result of experiments demonstrated that the proposed DMRNet not only achieves 7.15% greater accuracy than state-of-the-art methods but also successfully identifies some AD-related regions

Numerical Simulation of Coalescence-Induced Jumping of Multidroplets on Superhydrophobic Surfaces: Initial Droplet Arrangement Effect

The coalescence-induced droplet jumping on superhydrophobic surfaces (SHSs) has attracted considerable attention over the past several years. Most of the studies on droplet jumping mainly focus on two-droplet coalescence events whereas the coalescence of three or more droplets is actually more frequent and still remains poorly understood. In this work, a 3D lattice Boltzmann simulation is carried out to investigate the effect of initial droplet arrangements on the coalescence-induced jumping of three equally sized droplets. Depending on the initial position of droplets on the surface, the droplet coalescence behaviors can be generally classified into two types: one is that all droplets coalesce together instantaneously (concentrated configuration), and the other is that the initial coalesced droplet sweeps up the third droplet in its moving path (spaced configuration). The critical Ohnesorge number, <i>Oh</i>, for the transition of inertial-capillary-dominated coalescence to inertially limited-viscous coalescence is found to be 0.10 for droplet coalescence on SHSs with a contact angle of 160°. The jumping droplet velocity for concentrated multidroplet coalescence at <i>Oh</i> ⩽ 0.10 still follows the inertial-capillary scaling with an increased prefactor, which indicates a viable jumping droplet velocity enhancement scheme. However, the droplet jumping velocity is drastically reduced for the spaced configuration compared to that for the aforementioned concentrated configuration. Because <i>Oh</i> exceeds 0.10, the effects of initial droplet arrangements on multidroplet jumping become weaker as viscosity plays a key role in the merging process. This work will provide effective guidelines for the design of functional SHSs with enhanced droplet jumping for a wide range of industrial applications