Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans
<|reference_start|>Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans: 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.<|reference_end|>
@article{khatun2024hybridized,
    title={Hybridized Convolutional Neural Networks and Long Short-Term Memory for
  Improved Alzheimer's Disease Diagnosis from MRI Scans},
    author={Maleka Khatun, Md Manowarul Islam, Habibur Rahman Rifat, Md. Shamim
  Bin Shahid, Md. Alamin Talukder, Md Ashraf Uddin},
    journal={arXiv preprint arXiv:2403.05353},
    year={2024},
    doi={10.1109/ICCIT60459.2023.10441274},
    archivePrefix={arXiv},
    eprint={2403.05353},
    primaryClass={eess.IV cs.CV cs.LG}
}