Unveiling Alzheimer's Disease via MRI: Deep Learning Approaches for Accurate Detection
Abstract views: 9
/ 
PDF downloads: 2
Keywords:
Alzheimer's Disease (AD), Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN)Abstract
This study conducted a comparative analysis of machine learning algorithms to discern the most
effective approach for detecting Alzheimer's disease (AD). Timely and precise detection of AD stands
crucial for efficient intervention in this prevalent neurodegenerative disorder among the elderly. Deep
learning algorithms have demonstrated promising outcomes in AD diagnosis through the examination of
magnetic resonance imaging (MRI) scans. The research focused specifically on evaluating the performance
of diverse deep learning architectures, encompassing CNN (Convolutional Neural Network), in detecting
AD using MRI images. Utilizing a substantial dataset comprising MRI scans from both AD patients and
healthy individuals, the models were trained to automatically extract discriminative features from these
images. Experimental results underscore the effectiveness of the proposed models, notably the active use
of MobileNet and CNN, which achieved an impressive accuracy of 95.92% in identifying Alzheimer's
disease. These findings highlight the superior performance of CNN and MobileNet compared to DenseNet
and Inception V3 in AD detection, emphasizing their potential for accurate identification of AD compared
to other algorithms. Such insights offer valuable direction for selecting the most appropriate algorithm for
AD diagnosis, considering critical factors such as accuracy, computational efficiency, and resource
availability. However, further exploration and validation employing larger and more diverse datasets are
essential to establish the broader applicability and clinical relevance of these algorithms in real-world
scenarios for AD detection.
Downloads
References
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., & Song, X. (2020, March 9). Early detection of alzheimer’s disease using Magnetic Resonance Imaging: A novel approach combining convolutional neural networks and ensemble learning. Frontiers.
Amini, M., Pedram, M. M., Moradi, A., & Ouchani, M. (2021, April 28). Diagnosis of alzheimer’s disease severity with fmri images using robust multitask feature extraction method and Convolutional Neural Network (CNN). Computational and Mathematical Methods in Medicine.
C. Laske et al., “Innovative diagnostic tools for early detection of Alzheimer’s disease,” Alzheimer’s & Dementia, vol. 11, no. 5, pp. 561–578, May 2015, doi: 10.1016/J.JALZ.2014.06.004.
Dubey, S. (2019, December 26). Alzheimer’s dataset ( 4 class of images). Kaggle. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
P. M. Tostado, B. U. Pedroni, and G. Cauwenberghs, “Performance Trade-offs in Weight Quantization for Memory-Efficient Inference,” Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, pp. 246–250, Mar. 2019, doi: 10.1109/AICAS.2019.8771473.
Illakiya, T., & Karthik, R. (2023, March 8). Automatic detection of alzheimer’s disease using Deep Learning Models and neuro-imaging: Current trends and future perspectives - neuroinformatics. SpringerLink. https://link.springer.com/article/10.1007/s12021-023-09625-7
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications." arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). "Densely Connected Convolutional Networks." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700-4708.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). "Rethinking the Inception Architecture for Computer Vision." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818-2826.
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., & Song, X. (2020a, March 9). Early detection of alzheimer’s disease using Magnetic Resonance Imaging: A novel approach combining convolutional neural networks and ensemble learning. Frontiers. https://www.frontiersin.org/articles/10.3389/fnins.2020.00259/full
S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, “A novel ultrathin elevated channel low-temperature poly-Si TFT,” IEEE Electron Device Lett., vol. 20, pp. 569–571, Nov. 1999.
