Abstract
Early identification was essential in efficient the management of Alzheimer's disease (AD), which is one of the biggest causes of dementia. Conventional Medical Imaging (CMI) methods such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans are used to understand various aspects of health. However, they lack the ability to capture the dynamic progression of Alzheimer's disease without incorporating sequential data series. This research introduces a novel method that has been designed to address these limitations. By combining spatial-temporal analysis with dynamic sequence processing, the work presents the "Attention Enhanced CNN+LSTM" architecture. The proposed dual pipeline architecture combinesLSTeM (Long ShortTerm enhanced Memory) for LSTM processing with an Attention ResNet for CNN processing. This work designs a novel "CS-Attention Block" with "Aggregate Weighted Pooling" for the enhancement of ResNet-50 and integration of LSTeM; further, a QKV (Query-Key-Value) attention mechanism for feature fusion is also proposed. For experiments, the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset containing longitudinal images provides the MRI and PET scan image data used for the analysis of the model. Results: The proposed model showed better performance for early AD 12 months before clinical diagnosis. The model also achieved an accuracy of 0.9151, and the AUC reached a value of 0.9784. This result was further improved to be improved by0.9302 for the metric accuracy and 0.9913 for the metric AUC ahead of the 6-month prediction. The model fuses spatial-temporal features and processes the sequences to predict AD by addressing the limitations of existing models.
References
van Oostveen, Wieke M., and Elizabeth CM de Lange. "Imaging techniques in Alzheimer’s disease: a review of applications in early diagnosis and longitudinal monitoring." International journal of molecular sciences 22, no. 4 (2021): 2110.
Song, Juan, Jian Zheng, Ping Li, Xiaoyuan Lu, Guangming Zhu, and Peiyi Shen. "An effective multimodal image fusion method using MRI and PET for Alzheimer's disease diagnosis." Frontiers in digital health 3 (2021): 637386.
Janghel, R. R., and Y. K. Rathore. "Deep convolution neural network based system for early diagnosis of Alzheimer's disease." Irbm 42, no. 4 (2021): 258-267.
Mehmood, Atif, Shuyuan Yang, Zhixi Feng, Min Wang, AL Smadi Ahmad, Rizwan Khan, Muazzam Maqsood, and Muhammad Yaqub. "A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images." Neuroscience 460 (2021): 43-52.
Jo, Taeho, Kwangsik Nho, Shannon L. Risacher, Andrew J. Saykin, and Alzheimer’s Neuroimaging Initiative. "Deep learning detection of informative features in tau PET for Alzheimer’s disease classification." BMC bioinformatics 21 (2020): 1-13.
Raees, PC Muhammed, and Vinu Thomas. "Automated detection of Alzheimer’s Disease using Deep Learning in MRI." In Journal of Physics: Conference Series, vol. 1921, no. 1, IOP Publishing, 2021, 012024.
Murugan, S., Venkatesan, C., Sumithra, M. G., Gao, X. Z., Elakkiya, B., Akila, M., & Manoharan, S. “DEMNET: a deep learning model for early diagnosis of Alzheimer's diseases and dementia from MR images.” Ieee Access, 9, (2021),90319-90329.
Mohammed, Badiea Abdulkarem, Ebrahim Mohammed Senan, Taha H. Rassem, Nasrin M. Makbol, Adwan Alownie Alanazi, Zeyad Ghaleb Al-Mekhlafi, Tariq S. Almurayziq, and Fuad A. Ghaleb. "Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer’s disease based on deep learning and hybrid methods." Electronics 10, no. 22 (2021): 2860.
Kamada, Shin, Takumi Ichimura, and Toshihide Harada. "Image-Based Early Detection of Alzheimer’s Disease by Using Adaptive Structural Deep Learning." In Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference, pp. 595-605. Springer Singapore, 2021.
Hamdi, Mounir, Sami Bourouis, Kulhanek Rastislav, and Faizaan Mohmed. "Evaluation of neuro images for the diagnosis of Alzheimer's disease using deep learning neural network." Frontiers in Public Health 10 (2022): 834032.
Basheera, Shaik, and M. Satya Sai Ram. "Deep learning based Alzheimer's disease early diagnosis using T2w segmented gray matter MRI." International Journal of Imaging Systems and Technology 31, no. 3 (2021): 1692-1710.
Patil, Vijeeta, Manohar Madgi, and Ajmeera Kiran. "Early prediction of Alzheimer's disease using convolutional neural network: a review." The Egyptian Journal of Neurology, Psychiatry and Neurosurgery 58, no. 1 (2022): 130.
Salehi, Ahmad Waleed, Preety Baglat, Brij Bhushan Sharma, Gaurav Gupta, and Ankita Upadhya. "A CNN model: earlier diagnosis and classification of Alzheimer disease using MRI." In 2020 International Conference on Smart Electronics and Communication (ICOSEC), IEEE, 2020, 156-161.
Khagi, Bijen, and Goo-Rak Kwon. "3D CNN design for the classification of Alzheimer’s disease using brain MRI and PET." IEEE Access 8 (2020): 217830-217847.
Pan, Dan, An Zeng, Longfei Jia, Yin Huang, Tory Frizzell, and Xiaowei Song. "Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning." Frontiers in neuroscience 14 (2020): 259.
Ebrahimi, Amir, Suhuai Luo, and for the Alzheimer’S. Disease Neuroimaging Initiative. "Convolutional neural networks for Alzheimer’s disease detection on MRI images." Journal of Medical Imaging 8, no. 2 (2021): 024503-024503.
Alshammari, Majdah, and Mohammad Mezher. "A modified convolutional neural networks for MRI-based images for detection and stage classification of alzheimer disease." In 2021 National Computing Colleges Conference (NCCC), IEEE, 2021, 1-7.
Abugabah, Ahed, Atif Mehmood, Sultan Almotairi, and Ahmad AL Smadi. "Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images." Expert Systems 39, no. 9 (2022): e13003.
Duan, Junwei, Yang Liu, Huanhua Wu, Jing Wang, Long Chen, and CL Philip Chen. "Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain." Frontiers in Neuroscience 17 (2023): 1137567.
Ebrahim, Doaa, Amr MT Ali-Eldin, Hossam E. Moustafa, and Hesham Arafat. "Alzheimer disease early detection using convolutional neural networks." In 2020 15th international conference on computer engineering and systems (ICCES), IEEE, 2020, 1-6.
Rana, Md Masud, Md Manowarul Islam, Md Alamin Talukder, Md Ashraf Uddin, Sunil Aryal, Naif Alotaibi, Salem A. Alyami, Khondokar Fida Hasan, and Mohammad Ali Moni. "A robust and clinically applicable deep learning model for early detection of Alzheimer's." IET Image Processing 17, no. 14 (2023): 3959-3975.
Sun, Haijing, Anna Wang, Wenhui Wang, and Chen Liu. "An improved deep residual network prediction model for the early diagnosis of Alzheimer’s disease." Sensors 21, no. 12 (2021): 4182.
Ebrahimi, Amir, Suhuai Luo, and Raymond Chiong. "Introducing transfer learning to 3D ResNet-18 for Alzheimer’s disease detection on MRI images." In 2020 35th international conference on image and vision computing New Zealand (IVCNZ), IEEE, 2020, 1-6.
Odusami, Modupe, Rytis Maskeliūnas, Robertas Damaševičius, and Tomas Krilavičius. "Analysis of features of Alzheimer’s disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network." Diagnostics 11, no. 6 (2021): 1071.
Roy, Projapoti, Md Main Oddin Chisty, and HM Abdul Fattah. "Alzheimer’s disease diagnosis from MRI images using ResNet-152 Neural Network Architecture." In 2021 5th International Conference on Electrical Information and Communication Technology (EICT), IEEE, 2021, 1-6.
Odusami, Modupe, Rytis Maskeliūnas, Robertas Damaševičius, and Sanjay Misra. "ResD hybrid model based on ResNet18 and DenseNet121 for early Alzheimer disease classification." In International Conference on Intelligent Systems Design and Applications, Cham: Springer International Publishing, 2021, 296-305.
Liu, Sheng, Chhavi Yadav, Carlos Fernandez-Granda, and Narges Razavian. "On the design of convolutional neural networks for automatic detection of Alzheimer’s disease." In Machine Learning for Health Workshop, PMLR, 2020, 184-201.
Nguyen, Minh, Tong He, Lijun An, Daniel C. Alexander, Jiashi Feng, BT Thomas Yeo, and Alzheimer's Disease Neuroimaging Initiative. "Predicting Alzheimer's disease progression using deep recurrent neural networks." NeuroImage 222 (2020): 117203.