Alzheimer's disease (AD) is the most common long-term illness in elderly people. In recent years, deep learning has become popular in the area of medical imaging and has had a lot of success there. It has become the most effective way to look at medical images. When it comes to detecting AD, the deep neural model is more accurate and effective than general machine learning. Our research contributes to the development of a more comprehensive understanding and detection of the disease by identifying four distinct classes that are predictive of AD with a high weighted accuracy of 98.91%. A unique strategy has been proposed to improve the accuracy of the imbalance dataset classification problem via the combination of ensemble averaging models and five different transfer learning models in this study. EfficientNetB0+Resnet152(effnet+res152) and InceptionV3+EfficientNetB0+Resnet50(incep+effnet+res50) models have been fine-tuned and have reached the highest weighted accuracy for multi-class AD stage classifications.

翻译：阿尔茨海默病（AD）是老年人中最常见的长期疾病。近年来，深度学习在医学影像领域广受欢迎并取得了巨大成功，已成为分析医学图像最有效的方法。在检测AD方面，深度神经模型比传统机器学习方法更精确高效。本研究通过识别四个预测AD的独特类别（加权准确率高达98.91%），为更全面地理解和检测该疾病做出了贡献。本文提出了一种独特策略，通过集成集成平均模型与五种不同的迁移学习模型来提升不平衡数据集分类问题的准确率。其中EfficientNetB0+Resnet152(effnet+res152)和InceptionV3+EfficientNetB0+Resnet50(incep+effnet+res50)模型经过微调后，在AD多阶段分类任务中取得了最高加权准确率。