A number of life threatening neuro-degenerative disorders had degraded the
quality of life for the older generation in particular. Dementia is one such
symptom which may lead to a severe condition called Alzheimer's disease if not
detected at an early stage. It has been reported that the progression of such
disease from a normal stage is due to the change in several parameters inside
the human brain. In this paper, an innovative metaheuristic algorithms based
ViT model has been proposed for the identification of dementia at different
stage. A sizeable number of test data have been utilized for the validation of
the proposed scheme. It has also been demonstrated that our model exhibits
superior performance in terms of accuracy, precision, recall as well as
F1-score.

本文介绍了一种基于元启发式算法的 ViT 模型，用于在不同阶段识别老年人痴呆症，表现出较高的准确性、精确率、召回率和 F1 得分。

基于元启发式算法的视觉变换器模型在早期阿尔茨海默病诊断中的比较分析

A Comparative Analysis on Metaheuristic Algorithms Based Vision  Transformer Model for Early Detection of Alzheimer's Disease

Lacunes of presumed vascular origin (lacunes) are associated with an
increased risk of stroke, gait impairment, and dementia and are a primary
imaging feature of the small vessel disease. Quantification of lacunes may be
of great importance to elucidate the mechanisms behind neuro-degenerative
disorders and is recommended as part of study standards for small vessel
disease research. However, due to the different appearance of lacunes in
various brain regions and the existence of other similar-looking structures,
such as perivascular spaces, manual annotation is a difficult, elaborative and
subjective task, which can potentially be greatly improved by reliable and
consistent computer-aided detection (CAD) routines.
In this paper, we propose an automated two-stage method using deep
convolutional neural networks (CNN). We show that this method has good
performance and can considerably benefit readers. We first use a fully
convolutional neural network to detect initial candidates. In the second step,
we employ a 3D CNN as a false positive reduction tool. As the location
information is important to the analysis of candidate structures, we further
equip the network with contextual information using multi-scale analysis and
integration of explicit location features. We trained, validated and tested our
networks on a large dataset of 1075 cases obtained from two different studies.
Subsequently, we conducted an observer study with four trained observers and
compared our method with them using a free-response operating characteristic
analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits
performance similar to the trained human observers and achieves a sensitivity
of 0.974 with 0.13 false positives per slice. A feasibility study also showed
that a trained human observer would considerably benefit once aided by the CAD
system.

使用卷积神经网络的自动化方法可以高效地检测出脑血管源性腔隙，具有减少误检率的优势，并且可以促进神经退行性疾病的研究。