AI 中文总结
本综述探讨迁移学习在阿尔茨海默病诊断中的应用，评估其优势与局限，结合可解释人工智能，为该领域新研究者提供指导。
AI 中文摘要
全球数百万老年人正遭受阿尔茨海默病（简称AD）的困扰，这是一种广为人知的痴呆症形式，其特征为失忆、智力障碍和意识障碍。深度学习（DL）和机器学习（ML）模型无疑被用于在高维神经影像数据中识别AD相关模式，但它们需要全局优化，且存在过拟合问题，可能在测试数据集上产生不令人满意的结果。深度学习通过用卷积核对输入图像进行卷积来克服该问题，但MRI图像的任何突然变化或人为操作、图像预处理有限都可能误导卷积神经网络（CNN）实现高精度检测。迁移学习（TL）通过利用在大型数据集上预训练的模型来指导新模型在新神经影像数据集上的应用，已在AD诊断中证明了自身的价值。本综述全面概述了TL在分类、识别（包括AD转换）中的应用，评估了TL在数据有限时提高诊断准确性的优势与局限性，其独特之处在于将可解释人工智能纳入基于TL的AD诊断系统，最终该综述将为迁移学习诱导的神经退行性疾病检测领域的新研究者提供指导。
英文摘要
Worldwide, millions of senior citizens are suffering from Alzheimer disease abbreviated as AD, a well- versed form of dementia. AD is featured by amnesia, intellectual disability, and difficulty with consciousness. DL and ML models are undoubtedly explored to identify AD related patterns on large dimensional neuroimaging data but they need global optimization and are suffering from overfitting issue that might yield dissatisfactory result in testing data set. DL overcomes the issue by convolution of input image with kernel but any sudden change in the MRI image or human manipulation, limited pre- processing of the images can mislead CNN in achieving highly accurate detection. Transfer Learning (TL) has proved itself in AD diagnosis by utilizing pre-trained models on large data sets to guide novice model in a new neuroimaging dataset. This review provides an inclusive glimpse of TL implication in classification, identification including the conversion of AD. Keeping in view, we have assessed the strengths and limitations of TL in improvising diagnostic accuracy even with limited data. The uniqueness of the present review is the incorporation of explainable AI in TL based AD diagnosis system. Finally, it can be claimed that the review will guide the new re-searchers in the area of TL induced neurodegenerative disease detection.
Journal ref2025 AI-Driven Smart Healthcare for Society 5.0, Kolkata, India, 2025
DOI：10.1109/IEEECONF64992.2025.10962879
