Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning-based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain-MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention-aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning-inspired multistage conjoined twin network (HRAL-CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL-CTNN is efficiently overcoming the issues of not obtaining efficient attention-aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL-CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 $\pm$ 0.01% and F1 score of 99.30 $\pm$ 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.

中文翻译：

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一种基于智能分层剩余注意力学习的联合孪生神经网络，用于阿尔茨海默病阶段检测和预测

阿尔茨海默病 (AD) 会导致大脑细胞系统的记忆永久性受损，从而导致痴呆症的发生。在阿尔茨海默病的初始阶段及早发现对研究人员来说是一个挑战。基于深度学习和机器学习的技术可以帮助解决与大脑成像探索相关的许多问题。脑 MR 图像 (Brain-MRI) 用于在可计算研究工作中检测阿尔茨海默病。为了正确对阿尔茨海默病的阶段进行分类，需要从 MR 图像中提取判别性特征。最近，许多研究使用深度学习方法来早期检测这种疾病。然而，过度拟合会降低深度学习方法的性能，因为数据集的选择图像较小且不平衡。一些研究无法为阿尔茨海默病阶段分类提供更具辨别力和更有效的注意力意识特征，以提高模型性能。在本文中，我们开发了一种新颖的分层剩余注意力学习启发的多阶段联体双胞胎网络（HRAL-CTNN）来对阿尔茨海默病的阶段进行分类。我们使用增强方法来扩展不足和不平衡的数据。HRAL-CTNN 有效地克服了阿尔茨海默病阶段分类无法获得有效的注意力感知和生成特征的问题。该模型通过提取注意判别特征来解决冗余特征的问题，并通过数据增强来缩放不平衡数据，然后使用 HRAL-CTNN 进行训练和验证。这项拟议工作的执行是在 ADNI MRI 数据集上进行的。这项工作取得了 99.97 的出色准确率 $\pm$ 0.01%，F1 分数 99.30 $\pm$ 阿尔茨海默病分期分类为 0.02%。我们小组提出的模型在模型性能得分方面优于现有的相关研究。