基于元启发式算法的视觉Transformer模型用于阿尔茨海默病早期检测的对比分析
A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease
AI总结：
针对阿尔茨海默病早期检测需求，提出基于元启发式算法的ViT模型，经大量测试数据验证，在准确率、精确率等多项指标上表现更优。
摘要：
多种危及生命的神经退行性疾病尤其降低了老年群体的生活质量。痴呆便是这类症状之一，若未在早期发现，可能发展为称为阿尔茨海默病的严重病症。据报道，此类疾病从正常阶段进展的原因是人脑内多个参数发生变化。本文提出了一种创新的基于元启发式算法的ViT（视觉Transformer）模型，用于识别不同阶段的痴呆。研究使用了大量测试数据验证所提方案，结果表明，该模型在准确率、精确率、召回率以及F1分数方面均表现出更优性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
