Brain-related diseases are more sensitive than other diseases due to several factors, including the complexity of surgical procedures, high costs, and other challenges. Alzheimer's disease is a common brain disorder that causes memory loss and the shrinking of brain cells. Early detection is critical for providing proper treatment to patients. However, identifying Alzheimer's at an early stage using manual scanning of CT or MRI scans is challenging. Therefore, researchers have delved into the exploration of computer-aided systems, employing Machine Learning and Deep Learning methodologies, which entail the training of datasets to detect Alzheimer's disease. This study aims to present a hybrid model that combines a CNN model's feature extraction capabilities with an LSTM model's detection capabilities. This study has applied the transfer learning called VGG16 in the hybrid model to extract features from MRI images. The LSTM detects features between the convolution layer and the fully connected layer. The output layer of the fully connected layer uses the softmax function. The training of the hybrid model involved utilizing the ADNI dataset. The trial findings revealed that the model achieved a level of accuracy of 98.8%, a sensitivity rate of 100%, and a specificity rate of 76%. The proposed hybrid model outperforms its contemporary CNN counterparts, showcasing a superior performance.

翻译：脑部相关疾病因其手术复杂性、高昂成本及诸多挑战，比其他疾病更为敏感。阿尔茨海默病是一种常见的脑部疾病，会导致记忆力减退和脑细胞萎缩。早期发现对患者接受适当治疗至关重要。然而，通过手动扫描CT或MRI图像在早期阶段识别阿尔茨海默病颇具挑战。因此，研究人员深入探索基于计算机辅助的系统，采用机器学习和深度学习方法，通过训练数据集来检测阿尔茨海默病。本研究旨在提出一种混合模型，该模型结合了CNN模型的特征提取能力与LSTM模型的检测能力。本研究在混合模型中应用了名为VGG16的迁移学习技术，从MRI图像中提取特征。LSTM在卷积层与全连接层之间检测特征。全连接层的输出层采用softmax函数。模型的训练利用了ADNI数据集。试验结果显示，该模型达到了98.8%的准确率、100%的灵敏度以及76%的特异性。所提出的混合模型优于其同时代的CNN模型，展现出更优越的性能。