S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India.
Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, 600062, India.
Sci Rep. 2024 Sep 4;14(1):20622. doi: 10.1038/s41598-024-71299-1.
Alzheimer's Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI). Along with Artificial Intelligence (AI) techniques, the efficacy of identifying diseases related to the brain has become easier. But, the identical phenotype makes it challenging to identify the disease from the neuro-images. Hence, a deep learning method to detect AD at the beginning stage is suggested in this work. The newly implemented "Enhanced Residual Attention with Bi-directional Long Short-Term Memory (Bi-LSTM) (ERABi-LNet)" is used in the detection phase to identify the AD from the MRI images. This model is used for enhancing the performance of the Alzheimer's detection in scale of 2-5%, minimizing the error rates, increasing the balance of the model, so that the multi-class problems are supported. At first, MRI images are given to "Residual Attention Network (RAN)", which is specially developed with three convolutional layers, namely atrous, dilated and Depth-Wise Separable (DWS), to obtain the relevant attributes. The most appropriate attributes are determined by these layers, and subjected to target-based fusion. Then the fused attributes are fed into the "Attention-based Bi-LSTM". The final outcome is obtained from this unit. The detection efficiency based on median is 26.37% and accuracy is 97.367% obtained by tuning the parameters in the ERABi-LNet with the help of Modified Search and Rescue Operations (MCDMR-SRO). The obtained results are compared with ROA-ERABi-LNet, EOO-ERABi-LNet, GTBO-ERABi-LNet and SRO-ERABi-LNet respectively. The ERABi_LNet thus provides enhanced accuracy and other performance metrics compared to such deep learning models. The proposed method has the better sensitivity, specificity, F1-Score and False Positive Rate compared with all the above mentioned competing models with values such as 97.49%.97.84%,97.74% and 2.616 respective;y. This ensures that the model has better learning capabilities and provides lesser false positives with balanced prediction.
阿尔茨海默病（AD）会导致脑细胞逐渐死亡，这是由于脑细胞萎缩所致，这种情况在老年人中更为常见。在大多数情况下，AD 的症状被误认为是与年龄相关的压力。目前，最广泛使用的 AD 检测方法是磁共振成像（MRI）。随着人工智能（AI）技术的发展，识别与大脑相关疾病的效果变得更加容易。但是，相同的表型使得从神经影像中识别疾病变得具有挑战性。因此，本工作建议采用深度学习方法在早期阶段检测 AD。在检测阶段，新实现的“带有双向长短时记忆（Bi-LSTM）的增强残差注意力（ERABi-LNet）”用于从 MRI 图像中识别 AD。该模型用于提高 AD 检测的性能，提高 2%至 5%，降低错误率，平衡模型，从而支持多类问题。首先，将 MRI 图像提供给“残差注意力网络（RAN）”，该网络是专门使用三个卷积层（即空洞、扩张和深度可分离（DWS））开发的，以获取相关属性。这些层确定最合适的属性，并进行基于目标的融合。然后，将融合的属性输入到“基于注意力的 Bi-LSTM”中。最终结果从该单元获得。通过使用改进的搜索和救援操作（MCDMR-SRO）调整 ERABi-LNet 中的参数，基于中位数的检测效率为 26.37%，准确率为 97.367%。通过与 ROA-ERABi-LNet、EOO-ERABi-LNet、GTBO-ERABi-LNet 和 SRO-ERABi-LNet 分别进行比较，得到了 ERABi_LNet 的结果。与其他深度学习模型相比，ERABi_LNet 提供了更高的准确性和其他性能指标。与所有上述竞争模型相比，该方法具有更好的敏感性、特异性、F1 得分和假阳性率，其值分别为 97.49%、97.84%、97.74%和 2.616。这确保了该模型具有更好的学习能力，并提供了更少的假阳性和平衡的预测。
