Hybridized Convolutional Neural Networks and Long Short-Term Memory for
  Improved Alzheimer's Disease Diagnosis from MRI Scans
Maleka Khatun Md Manowarul Islam Habibur Rahman Rifat Md. Shamim Bin Shahid Md. Alamin Talukder Md Ashraf Uddin
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.