Designing an Intelligent System for Predicting Alzheimer’s Disease
Keywords:Densenet169, Convolution neural network, Alzheimer disease prediction, deep learning, Transfer learning
Alzheimer's disease (AD) is a degenerative progressive disorder that affects the brain's neurons and nerve cells, causing behavioral changes, memory loss, language skills, and thinking. It is a neurological condition with an exponentially increasing incidence rate, primarily affecting adults over 65. Contrary to popular belief, AD is not a normal aspect of aging and is the most prevalent type of dementia. In this work, CNN, Densenet169, and the Hybrid convolution recurrent neural network approach are used to detect Alzheimer's disease at an early stage. Data augmentation is utilized at preprocessing step to handle the small size of the dataset. The Hybrid CNN-RNN network design comprises convolution layers followed by a recurrent neural network (RNN). The combined model uses the RNN to extract relationships from MRI images and to account for temporal dependencies of the images during classification. Three algorithms are used for classifying AD and comparing their results. We have tested the model on MRI dataset. According to the results, the proposed CNN algorithm achieved higher accuracy than the Densenet169 and the hybrid Convolution-Recurrent Neural Network.
A. Alberdi, A. Aztiria, and A. Basarab, “On the early diagnosis of Alzheimer’s disease from multimodal signals : A survey,” Artif. Intell. Med., vol. 71, pp. 1–29, 2016. https://doi.org/10.1016/j.artmed.2016.06.003.
S. El Sappagh, J. M. Alonso, S. M. R. Islam, and A. M. Sultan, “A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease,” Sci. Rep., pp. 1–26, 2021. https://doi.org/10.1038/s41598-021-82098-3.
M. Orouskhani, C. Zhu, S. Rostamian, F. S. Zadeh, M. Shafiei, and Y. Orouskhani, “Alzheimer ’ s disease detection from structural MRI using conditional deep triplet network,” Neurosci. Informatics, vol. 2, no. 4, p. 100066, 2022. https://doi.org/10.1016/j.neuri.2022.100066.
W. Jagust, “Imaging the evolution and pathophysiology of Alzheimer disease,” Nat. Rev. Neurosci., vol. 19, no. 11, pp. 687–700, 2018. https://doi.org/10.1038/s41583-018-0067-3.
P. C. M. Raees and V. Thomas, “Automated detection of Alzheimer ’ s disease using deep learning in MRI,” J. Phys. Conf. Ser. Pap., vol. 1921, no. 1, p. 012024, 2021. https://doi.org/10.1088/1742-6596/1921/1/012024.
C. Park, J. Ha, and S. Park, “Prediction of Alzheimer’s disease based on deep neural network by integrating gene expression and DNA methylation dataset,” Expert Syst. Appl., vol. 140, p. 112873, 2020. https://doi.org/10.1016/j.eswa.2019.112873.
S. Rathore, M. Habes, A. Iftikhar, A. Shacklett, and C. Davatzikos, “A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages,” Neuroimage, vol. 155, pp. 530-548, 2017. https://doi.org/10.1016/j.neuroimage.2017.03.057.
C. Plant et al., “Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease,” Neuroimage, vol. 50, no. 1, pp. 162–174, 2010. https://doi.org/10.1016/j.neuroimage.2009.11.046.
L. Wang, P. Li, M. Hou, X. Zhang, X. Cao, and H. Li, “Construction of a risk prediction model for Alzheimer’s disease in the elderly population,” BMC Neurol., vol. 21, no. 1, pp. 1–10, 2021. https://doi.org/10.1186/s12883-021-02276-8.
G. Lee, K. Nho, B. Kang, K. Sohn, and D. Kim, “Predicting Alzheimer’s disease progression using multi-modal deep learning approach,” Sci. Rep., vol. 9, no. 1, pp. 1–12, 2019.
A. Shikalgar and S. Sonavane, “Hybrid deep learning approach for classifying alzheimer disease based on multimodal data,” in Computing in Engineering and Technology, Springer, 2020, pp. 511-520. https://doi.org/10.1007/978-981-32-9515-5_49.
Z. Kong, M. Zhang, W. Zhu, Y. Yi, T. Wang, and B. Zhang, “Multi-modal data Alzheimer’s disease detection based on 3D convolution,” Biomed. Signal Process. Control, vol. 75, p. 103565, 2022.
H. Ji, Z. Liu, W. Q. Yan, and R. Klette, “Early diagnosis of Alzheimer’s disease using deep learning,” Proceedings of the 2nd International Conference on Control and Computer Vision, 2019, pp. 87–91. https://doi.org/10.1145/3341016.3341024.
W. N. Ismail, F. R. P. P, and M. A. S. Ali, “MULTforAD: Multimodal MRI neuroimaging for Alzheimer’s disease detection based on a 3D convolution model,” Electronics, vol. 11, no. 23, p. 3893, 2022.. https://doi.org/10.3390/electronics11233893
A. Loddo, S. Buttau, and C. Di Ruberto, “Deep learning based pipelines for Alzheimer’s disease diagnosis: a comparative study and a novel deep-ensemble method,” Comput. Biol. Med., vol. 141, p. 105032, 2022. https://doi.org/10.1016/j.compbiomed.2021.105032.
P. Y. Simard, D. Steinkraus, J. C. Platt, and others, “Best practices for convolutional neural networks applied to visual document analysis,” Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., Edinburgh, UK, 2003, pp. 958-963, https://doi.org/10.1109/ICDAR.2003.1227801.
A. Kaya, A. Seydi, C. Catal, H. Yalin, and H. Temucin, “Analysis of transfer learning for deep neural network based plant classification models,” Comput. Electron. Agric., vol. 158, no. January, pp. 20–29, 2019. https://doi.org/10.1016/j.compag.2019.01.041.
J. Nam and S. Kim, “Heterogeneous defect prediction,” Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, 2015, pp. 508–519. https://doi.org/10.1145/2786805.2786814.
K. Thenmozhi and U. S. Reddy, “Crop pest classification based on deep convolutional neural network and transfer learning,” Comput. Electron. Agric., vol. 164, no. July, p. 104906, 2019. https://doi.org/10.1016/j.compag.2019.104906.
A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, 2018. https://doi.org/10.1016/j.compag.2018.02.016.
N. Jain, S. Kumar, A. Kumar, P. Shamsolmoali, and M. Zareapoor, “Hybrid deep neural networks for face emotion recognition,” Pattern Recognit. Lett., vol. 115, pp. 101–106, 2018. https://doi.org/10.1016/j.patrec.2018.04.010.
K. A. Alafandy, H. Omara, M. Lazaar, and M. Al Achhab, “Investment of classic deep CNNs and SVM for classifying remote sensing images investment of classic deep CNNs and SVM for classifying remote sensing images,” Adv. Sci. Technol. Eng. Syst. J., vol. 5, no. 5, pp. 652–659, 2020. https://doi.org/10.25046/aj050580.
A. Vulli, P. N. Srinivasu, M. S. K. Sashank, J. Shafi, J. Choi, and M. F. Ijaz, “Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-Cycle policy,” Sensors, vol. 22, no. 8, 2022. https://doi.org/10.3390/s22082988.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997. https://doi.org/10.1162/neco.19220.127.116.115.
A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020. https://doi.org/10.1016/j.physd.2019.132306.
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