Novel Deep-Learning Approach for Automatic Diagnosis of Alzheimer’s Disease from MRI
Source abstract
Abstract
This study introduces a novel deep-learning methodology that is customized to automatically diagnose Alzheimer’s disease (AD) through the analysis of MRI datasets. The process of diagnosing AD via the visual examination of magnetic resonance imaging (MRI) presents considerable challenges. The visual diagnosis of mild to very mild stages of AD is challenging due to the MRI similarities observed between a brain that is aging normally and one that has AD. The detection of AD with extreme precision is critical during its early stages. Deep-learning techniques have recently been shown to be significantly more effective than human detection in identifying various stages of AD, enabling early-stage diagnosis. The aim of this research is to develop a deep-learning approach that utilizes pre-trained convolutional neural networks (CNNs) to accurately detect the severity levels of AD, particularly in situations where the quantity and quality of available datasets are limited. In this approach, the AD dataset is preprocessed via a refined image processing module prior to the training phase. The proposed method was compared to two well-known deep-learning algorithms (VGG16 and ResNet50) using four Kaggle AD datasets: one for the normal stage of the disease and three for the mild, very mild, and moderate stages, respectively. This allowed us to evaluate the effectiveness of the classification results. The three models were compared using six performance metrics. The results achieved with our approach indicate an overall detection accuracy of 99.3%, which is superior to the other existing models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Retinal Imaging and Analysis · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
Department of Physics and Astronomy, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Funding
The Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia
References (46)
Show source-provided references
1. Deep Learning Approach for Early Detection of Alzheimer’s Disease10.1007/s12559-021-09946-2 ↗
2. Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using an Ensemble System of Deep Convolutional Neural Networks10.1186/s40708-018-0080-3 ↗
3. Andrushia, A.D., Sagayam, K.M., Dang, H., Pomplun, M., and Quach, L. (2021). Visual-Saliency-Based Abnormality Detection for MRI Brain Images—Alzheimer’s Disease Analysis. Appl. Sci., 11.10.3390/app11199199 ↗
4. Deep Learning Based Multilevel Classification of Alzheimer’s Disease Using MRI Scans10.1088/1757-899X/1084/1/012017 ↗
5. Sethi, M., Ahuja, S., Rani, S., Koundal, D., Zaguia, A., and Enbeyle, W. (2022). An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network. BioMed Res. Int., 2022.10.1155/2022/8739960 ↗
6. Detection and stage wise classification of Alzheimer disease using deep learning methods
7. Sadat, S.U., Shomee, H.H., Awwal, A., Amin, S.N., Reza, T., and Parvez, M.Z. (2021, January 17–20). Alzheimer’s disease detection and classification using transfer learning technique and ensemble on Convolutional Neural Networks. Proceedings of the 2021 IEEE Interna-tional Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia.10.1109/SMC52423.2021.9659179 ↗
8. Vrahatis, A.G., Skolariki, K., Krokidis, M.G., Lazaros, K., Exarchos, T.P., and Vlamos, P. (2023). Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors, 23.10.3390/s23094184 ↗
9. Deep learning10.1038/nature14539 ↗
10. Danker, A., and Wirgård Wiklund, J. (2021). Using Transfer Learning to Classify Different Stages of Alzheimer’s Disease, KTH Royal INSTITUTE of Technology.
11. Saleem, T.J., Zahra, S.R., Wu, F., Alwakeel, A., Alwakeel, M., Jeribi, F., and Hijji, M. (2022). Deep Learning-Based Diagnosis of Alzheimer’s Disease. J. Pers. Med., 12.10.3390/jpm12050815 ↗
12. Deep learning for Internet of Things data analytics10.1016/j.procs.2019.12.120 ↗
13. Suk, H.I., and Shen, D. (2016). International Workshop on Machine Learning in Medical Imaging, Springer.
14. Billones, C.D., Demetria, O.J.L.D., Hostallero, D.E.D., and Naval, P.C. (2016, January 22–25). DemNet: A convolutional neural network for the detection of Alzheimer’s disease and mild cognitive impairment. Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore.10.1109/TENCON.2016.7848755 ↗
15. Sarraf, S., and Tofighi, G. (2016). Classification of alzheimer’s disease using fmri data and deep learning convolutional neural networks. arXiv.
16. Sarraf, S., and Tofighi, G. (2016). Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks. arXiv.
17. Gunawardena, K.A.N.N.P., Rajapakse, R.N., and Kodikara, N.D. (2017, January 21–23). Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural MRI data. Proceedings of the 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland, New Zealand.10.1109/M2VIP.2017.8211486 ↗
18. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks10.1016/j.nicl.2018.101645 ↗
19. Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling10.1007/s10916-018-0932-7 ↗
20. Karasawa, H., Liu, C.-L., and Ohwada, H. (2018). Asian Conference on Intelligent Information and Database Systems, Springer.
21. Goceri, E. (2019). Diagnosis of Alzheimer’s disease with Sobolev gradient-based optimization and 3D convolutional neural network. Int. J. Numer. Methods Biomed. Eng., 35.10.1002/cnm.3225 ↗
22. Tang, H., Yao, E., Tan, G., and Guo, X. (2018). International CCF Conference on Artificial Intelligence, Springer.
23. Spasov, S.E., Passamonti, L., Duggento, A., Liò, P., and Toschi, N. (2018, January 18–21). A multi-modal convolutional neural network framework for the prediction of Alzheimer’s disease. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.10.1109/EMBC.2018.8512468 ↗
24. Convolution neural network–based Alzheimer’s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation10.1016/j.trci.2019.10.001 ↗
25. Classification of Alzheimer’s disease via eight-layer convolutional neural network with batch normalization and dropout techniques10.1166/jmihi.2020.3001 ↗
26. Mehmood, A., Maqsood, M., Bashir, M., and Shuyuan, Y. (2020). A deep siamese convolution neural network for multi-class classification of alzheimer disease. Brain Sci., 10.10.3390/brainsci10020084 ↗
27. Multi-class diagnosis of Alzheimer’s disease using cascaded three dimensional convolutional neural network10.1007/s13246-020-00924-w ↗
28. Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer’s disease10.1007/s11548-019-02106-w ↗
29. Dyrba, M., Hanzig, M., Altenstein, S., Bader, S., Ballarini, T., Brosseron, F., Buerger, K., Cantré, D., Dechent, P., and Dobisch, L. (2020). Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer’s disease. arXiv.10.1186/s13195-021-00924-2 ↗
30. Automated MRI-based deep learning model for detection of Alzheimer’s disease process10.1142/S012906572050032X ↗
31. Solano-Rojas, B., and Villalón-Fonseca, R. (2021). A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery. Sensors, 21.10.3390/s21041302 ↗
32. Tan, M., and Le, Q.V. (2019, January 10–15). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA.
33. Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network10.1016/j.asoc.2020.106691 ↗
34. Agarwal, D., Berbis, M.A., Martín-Noguerol, T., Luna, A., Garcia, S.C.P., and de la Torre-Díez, I. (2022). End-to-end deep learning architectures using 3D neuroimaging biomarkers for early Alzheimer’s diagnosis. Mathematics, 10.10.3390/math10152575 ↗
35. Automated Medical Diagnosis of Alzheimer’s Disease Using an Efficient Net Convolutional Neural Network10.1007/s10916-023-01941-4 ↗
36. Luz, E., Silva, P.L., Silva, R., Silva, L., Guimarães, J., Miozzo, G., Moreira, G., and Menotti, D. (2021). Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. arXiv.10.1007/s42600-021-00151-6 ↗
37. COVID-19 Detection and Disease Progression Visualization: Deep Learning on Chest X-rays for Classification and Coarse Localization10.1007/s10489-020-01867-1 ↗
38. Dubey, S. (2020, November 29). Alzheimer’s Dataset (4 Class of Images). Available online: https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images.
39. Automated Classification of Alzheimer’s Disease Based on MRI Image Processing Using Convolutional Neural Network (CNN) With AlexNet Architecture10.1088/1742-6596/1844/1/012020 ↗
40. (2023, March 20). Atlases—NIST, Available online: https://nist.mni.mcgill.ca/atlases/.
41. Samuelson, F.W., and Taylor-Phillips, S. (2021, January 15–19). Towards a Quantitative Analysis of Class Activation Mapping for Deep Learning-Based Computer-Aided Diagnosis. Proceedings of the Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, Online.
42. Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI10.3389/fnagi.2022.876202 ↗
43. Kabani, A., and El-Sakka, M.R. (2016, January 13–15). Object Detection and Localization Using Deep Convolutional Networks with Softmax Activation and Multi-class Log Loss. Proceedings of the 2016 International Conference on Image Analysis and Recognition (ICIAR 2016), Póvoa de Varzim, Portugal.10.1007/978-3-319-41501-7_41 ↗
44. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20–25). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
45. A Comparative Study of Alzheimer’s Disease Classification using Multiple Transfer Learning Models10.33851/JMIS.2019.6.4.209 ↗
46. The Effect of Batch Size on the Generalizability of the Convolutional Neural Networks on a Histopathology Dataset10.1016/j.icte.2020.04.010 ↗