JCEEES

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

Index
Review
Detection of Alzheimer's disease from magnetic resonance images with deep learning
Alzheimer’s disease (AD) is a neurodegenerative process that leads to irreversible cognitive impairment in the elderly population and constitutes a significant socio-economic burden on a global scale. The lack of a definitive cure for the disease has made early diagnosis strategies based on magnetic resonance imaging (MRI) data critical. This study offers an innovative perspective to the literature by systematically examining current deep learning approaches used in AD diagnosis. The review includes a comprehensive technical evaluation of innovative preprocessing techniques, specialized convolutional neural network (CNN) architectures, and different input representation strategies (2D, 3D, and stacked sections) used in the process from raw data to clinical prediction. Focusing on methodological gaps in the existing literature, this study discusses key obstacles threatening diagnostic validity, such as class imbalance and data leakage, and highlights application suggestions for overcoming these problems. Ultimately, the research gaps identified in light of the findings and the practical solutions offered aim to contribute to the design of next-generation diagnostic systems by providing researchers with a strategic roadmap in terms of model generalizability and clinical integration.


1. Abrol, A., Bhattarai, M., Fedorov, A., Du, Y., Plis, S., & Calhoun, V. (2020). Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease. Journal of Neuroscience Methods, 339, 108701. https://doi.org/10.1016/j.jneumeth.2020.108701
2. Aderghal, K., Khvostikov, A., Krylov, A., Benois-Pineau, J., Afdel, K., & Catheline, G. (2018). Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 345-350. https://doi.org/10.1109/CBMS.2018.00067
3. Afzal, S., Maqsood, M., Nazir, F., Khan, U., Aadil, F., Awan, K. M., ... & Song, O. Y. (2019). A data augmentation-based framework to handle class imbalance problem for Alzheimer’s stage detection. IEEE Access, 7, 115528-115539. https://doi.org/10.1109/ACCESS.2019.2932786
4. Ahmad, M. (2024). EfficientNetV2 and MobileNetV2-based lightweight deep learning framework for Alzheimer’s disease classification. Scientific Reports, 14, 11234. https://doi.org/10.1038/s41598-024-61234-x
5. Alzheimer’s Association. (2015). 2015 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 11(3), 332-384. https://doi.org/10.1016/ j.jalz.2015.02.003
6. Alzheimer’s Association. (2016). 2016 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 12(4), 459-509. https://doi.org/10.1016/ j.jalz.2016.03.001
7. Arafa, D. A., Moustafa, H. E. D., Ali, H. A., Ali-Eldin, A. M., & Saraya, S. F. (2024). A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images. Multimedia Tools and Applications, 83(2), 3767-3799. https://doi.org/10.1007/s11042-023-15738-7
8. Ardalan, Z., & Subbian, V. (2022). Transfer learning approaches for neuroimaging analysis: a scoping review. Frontiers in Artificial Intelligence, 5, 780405. https://doi.org/10.3389/frai.2022.780405
9. Avants, B. B., Tustison, N., & Song, G. (2009). Advanced normalization tools (ANTs). Insight Journal, 2, 1-35.
10. Basaia, S., Agosta, F., Wagner, L., Canu, E., Magnani, G., Santangelo, R., ... & ADNI. (2019). Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical, 21, 101645. https://doi.org/10.1016/j.nicl.2018.101645
11. Bi, X. A., Hu, X., Wu, H., & Wang, Y. (2020). Multimodal data analysis of Alzheimer’s disease based on clustering evolutionary random forest. IEEE Journal of Biomedical and Health Informatics, 24(10), 2973-2983. https://doi.org/10.1109/JBHI.2020.2975767
12. Breijyeh, Z., & Karaman, R. (2020). Comprehensive review on Alzheimer’s disease: Causes and treatment. Molecules, 25(24), 5789. https://doi.org/10.3390/molecules25245789
13. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research,16, 321-357. https://doi.org/10.1613/jair.953
14. Chen, Y., Wang, L., Ding, B., Shi, J., Wen, T., Huang, J., & Ye, Y. (2024). Automated Alzheimer’s disease classification using deep learning models with Soft-NMS and improved ResNet50 integration. Journal of Radiation Research and Applied Sciences, 17(1), 100782. https://doi.org/ 10.1016/j.jrras.2023.100782
15. Dadar, M., Manera, A. L., Ducharme, S., & Collins, D. L. (2022). White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer’s disease and frontotemporal dementia. Neurobiology of Aging, 111, 54-63. https://doi.org/10.1016/j.neurobiolaging.2021.11.002
16. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). “BERT: pretraining of deep bidirectional transformers for language understanding,” in NAACL HLT 2019-2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies-Proceedings of the Conference, Vol. 1 (Minneapolis, MN), 4171-4186.
17. Ding, Y., Sohn, J. H., Kawczynski, M. G., Trivedi, H., Harnish, R., Jenkins, N. W., ... & ADNI. (2019). A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology, 290(2), 456-464. https://doi.org/10.1148/radiol.2018180926
18. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010. 11929.
19. Ebrahimi, A., Luo, S., & ADNI. (2021). Convolutional neural networks for Alzheimer’s disease detection on MRI images. Journal of Medical Imaging, 8(2), 024503. https://doi.org/10.1117/1.JMI.8.2.024503
20. Elharrouss, O., Akbari, Y., Almaadeed, N., & Al-Maadeed, S. (2022). Backbones-review: feature extraction networks for deep learning and deep reinforcement learning approaches. arXiv Preprint arXiv:2206. 08016.
21. Ellis, K. A., Bush, A. I., Darby, D., De Fazio, D., Foster, J., Hudson, P., ... & AIBL. (2009). The Australian imaging, biomarkers and lifestyle (AIBL) study of aging. International Psychogeriatrics, 21(4), 672-687. https://doi.org/10.1017/S104161020900911X
22. Farooq, A., Anwar, S., Awais, M., & Rehman, S. (2017). A deep CNN based multi-class classification of Alzheimer’s disease using MRI. 2017 IEEE International Conference on Imaging Systems and Techniques (IST), 1-6. https://doi.org/10.1109/IST.2017.8261460
23. Farooq, A., Anwar, S., Awais, M., and Alnowami, M. (2017). “Artificial intelligence based smart diagnosis of Alzheimer’s disease and mild cognitive impairment.” In 2017 International Smart Cities Conference, ISC2 2017 (Institute of Electrical and Electronics Engineers Inc.). https://doi.org/10.1109/ISC2.2017.8090871
24. Feng, W., Halm-Lutterodt, N. V., Tang, H., Mecum, A., Mesregah, M. K., Ma, Y., ... & ADNI. (2020). Automated MRI-based deep learning model for detection of Alzheimer’s disease process. International Journal of Neural Systems, 30(6), 2050032. https://doi.org/10.1142/S0129065720 50032X
25. Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774-781. https://doi.org/10.1016/j.neuroimage.2012.01.021
26. Han, Y. F., & Kaushik, B. (2020). Computer vision technique for neuro-image analysis in neurodegenerative diseases: A survey. 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 346-350. https://doi.org/10.1109/ESCI48226.2020.9167645
27. Han, Y. F., & Kaushik, B. (2021). Neuro-image classification for prediction of Alzheimer’s disease using machine learning techniques. Machine Intelligence and Data Science Applications, 483-493. https://doi.org/10.1007/978-981-33-4046-6_40
28. He, K., Chen, X., Xie, S., Li, Y., Doll, P., & Girshick, R. (2021). Masked Autoencoders are Scalable Vision Learners New Orleans, LA: IEEE. https://doi.org/10.1109/CVPR52688.2022.01553
29. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE CVPR, 770-778. https://doi.org/10.1109/CVPR.2016.90
30. Hızır, L., Ramírez, J., Gorriz, J. M., Brahim, A., Segovia, F., & ADNI. (2015). Early diagnosis of Alzheimer’s disease based on PLS, PCA and segmented MRI images. Neurocomputing, 151, 139-150. https://doi.org/ 10.1016/j.neucom.2014.09.070
31. Hosseini-Asl, E., Gimel’farb, G., & El-Baz, A. (2016a). Alzheimer’s Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network Frontiers in BioscienceLandmark.
32. Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016b). “Alzheimer’s disease diagnostics by adaptation of 3D convolutional network.” Proceedings-International Conference on Image Processing, ICIP (Phoenix, AZ), 126-130. https://doi.org/10.1109/ICIP.2016.7532332
33. Hu, S., Yu, W., Chen, Z., & Wang, S. (2020). “Medical image reconstruction using generative adversarial network for Alzheimer disease assessment with class-imbalance problem.” In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (Chengdu: IEEE), 1323-1327. https://doi.org/10.1109/ICCC51575.2020.9344912
34. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE CVPR, 4700-4708. https://doi.org/10.1109/CVPR.2017.243
35. Hür, G. (2021). PRISMA kontrol listesi 2020 güncellemesi. Online Türk Sağlık Bilimleri Dergisi, 6(4), 603-605. https://doi.org/10.26453/otshbd. 941914
36. Islam, J., & Zhang, Y. (2018). Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Informatics, 5(2), 2. https://doi.org/10.1186/s40708-018-0080-3
37. Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., ... & ADNI. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685-691. https://doi.org/10.1002/jmri.21049
38. Jain, R., Jain, N., Aggarwal, A., & Hemanth, D. J. (2019). Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research, 57, 147-159. https://doi.org/10.1016/j.cogsys.2018.12.017
39. Jain, R., Jain, N., Aggarwal, A., & Hemanth, D. J. (2019). Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cognitive Systems Research, 57, 147-159. https://doi.org/10.1016/j.cogsys.2018.12.015
40. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782-790. https://doi.org/10. 1016/j.neuroimage.2011.09.015
41. Khan, N. M., Abraham, N., & Hon, M. (2019). Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease. IEEE Access 7, 72726-72735. https://doi.org/10.1109/ACCESS.2019.2920 448
42. Khedher, L., Ramírez, J., Górriz, J. M., Brahim, A., & Segovia, F. (2015). Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented mri images. Neurocomputing, 151, 139-150. https://doi.org/ 10.1016/j.neucom.2014.09.072
43. Korolev, S., Safiullin, A., Belyaev, M., & Dodonova, Y. (2017). Residual and plain convolutional neural networks for 3D brain MRI classification. 2017 IEEE ISBI, 835-838. https://doi.org/10.1109/ISBI.2017.7950647
44. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
45. Kundaram, S. S., & Pathak, K. C. (2021). Deep learning based Alzheimer’s disease detection. 4th International Conference on Microelectronics, Computing and Communication Systems, 587-597. https://doi.org/10.1007/978-981-15-5546-6_52
46. LaMontagne, P. J., Benzinger, T. L. S., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., ... & ADNI. (2019). OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. medRxiv. https://doi.org/10.1101/2019.12.13.19014902
47. Lebedev, A. V., Westman, E., Van Westen, G. J., Kramberger, M. G., Lundervold, A., Aarsland, D., ... & ADNI. (2014). Random forest ensembles for detection and prediction of Alzheimer’s disease. NeuroImage: Clinical, 6, 115-125. https://doi.org/10.1016/j.nicl.2014.08. 016
48. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2323. https://doi.org/10.1109/5.726791
49. Li, F., Tran, L., Thung, K. H., Ji, S., Shen, D., & Li, J. (2015). A robust deep model for improved classification of AD/MCI patients. IEEE Journal of Biomedical and Health Informatics, 19(5), 1610-1616. https://doi.org/10.1109/JBHI.2015.2429556
50. Lian, C., Liu, M., Zhang, J., & Shen, D. (2020). Hierarchical fully convolutional network for joint atrophy localization and alzheimer’s disease diagnosis using structural MRI. IEEE Transactions on Pattern Analysis and Machine Intelligence,42(4), 880-893. https://doi.org/10. 1109/TPAMI.2018.2889096
51. Lim, B. Y., Lai, K. W., Haiskin, K., Kulathilake, K. A. S. H., Ong, Z. C., Hum, Y. C., ... & ADNI. (2022). Deep learning model for prediction of progressive MCI to Alzheimer’s disease using structural MRI. Frontiers in Aging Neuroscience, 14, 876202. https://doi.org/10.3389/fnagi.2022. 876202
52. Lin, W., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., ... & Alzheimer’s Disease Neuroimaging Initiative. (2018). Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Frontiers in Neuroscience,12, 777. https://doi.org/10.3389/fnins.2018.00777
53. Liu, J., Li, M., Luo, Y., Yang, S., Li, W., & Bi, Y. (2021). Alzheimer’s disease detection using depthwise separable convolutional neural networks. Computer Methods and Programs in Biomedicine,203, 106417. https://doi.org/10.1016/j.cmpb.2021.106417
54. Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Shen, L., ... & Alzheimer’s Disease Neuroimaging Initiative. (2020). A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. NeuroImage 208, 116459. https://doi.org/10.1016/j.neuroimage.2019.116459
55. Liu, M., Zhang, D., & Shen, D. (2016). Relationship induced multi-template learning for diagnosis of Alzheimer’s disease and mild cognitive impairment. IEEE Transactions on Medical Imaging, 35(6), 1463-1474. https://doi.org/10.1109/TMI.2016.2515025
56. Liu, M., Zhang, J., Adeli, E., & Shen, D. (2018). Landmark-based deep multi-instance learning for brain disease diagnosis. Medical Image Analysis, 43, 157-168. https://doi.org/10.1016/j.media.2017.10.005
57. Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (2014). “Early diagnosis of Alzheimer’s disease with deep learning,” In 2014 IEEE 11th International Symposium on Biomedical Imaging (Beijing: IEEE), 1015-1018. https://doi.org/10.1109/ISBI.2014.6868045
58. Malone, I. B., Cash, D., Ridgway, G. R., MacManus, D. G., Ourselin, S., Fox, N. C., ... & ADNI. (2013). MIRIAD-public release of a multiple time point Alzheimer’s MR imaging dataset. NeuroImage, 70, 33-36. https://doi.org/10.1016/j.neuroimage.2012.12.044
59. Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2010). Open Access series of imaging studies: Longitudinal MRI data. Journal of Cognitive Neuroscience, 22(12), 2677-2684. https://doi.org/10.1162/jocn.2009.21407
60. Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open access series of imaging studies (OASIS): Cross-sectional MRI data. Journal of Cognitive Neuroscience, 19(9), 1498-1507. https://doi.org/10.1162/jocn.2007.19.9.1498
61. Moradi, E., Pepe, A., Gaser, C., Huttunen, H., & Tohka, J. (2015). Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage, 104, 398-412. https://doi.org/10.1016/j.neuroimage.2014.10.002
62. Murugan, S., Venkatesan, C., Sumithra, M. G., Gao, X. Z., Elakkiya, B., Akila, M., & Manoharan, S. (2021). Demnet: a deep learning model for early diagnosis of alzheimer diseases and dementia from mr images. IEEE Access 9, 90319-90329. https://doi.org/10.1109/ACCESS.2021.3090474
63. Nasir, A., Tamur, M., & Azhar, A. (2021). Computer-aided COVID-19 diagnosis and comparison of deep learners using augmented CXR. Journal of X-Ray Science and Technology, 30(1), 1-21. https://doi.org/10. 3233/XST-210967
64. Odusami, M., Maskeliunas, R., Damaševicius, R., and Krilavicius, T. (2021). 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, 1071. doi: 10.3390/diagnostics11061071
65. Padmavathi, B., Deeksha, R., Darshitha, H., & Ashwath, B. (2023). Alzheimer classification using Deep Learning technique.Journal of Survey in Fisheries Sciences, 10(3S), 2854-2864.
66. Payan, A., and Montana, G. (2015). “Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks.” In ICPRAM 2015-4th International Conference on Pattern Recognition Applications and Methods, Vol. 2 (Lisbon), 355-362.
67. Poloni, K. M., & Ferrari, R. J. (2022). Automated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer’s disease. Computer Methods and Programs in Biomedicine, 214, 106581. https://doi.org/10.1016/j.cmpb.2021.106581
68. Qiu, S., Joshi, P. S., Miller, M. I., Xue, C., Zhou, X., Karjadi, C., ... & Kolachalama, V. B. (2020). Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain, 143, 1920-1933. https://doi.org/10.1093/brain/awaa 137
69. Rao, P., & Kumar, S. (2025). Real-time Alzheimer’s stage detection using YOLOv11 and 3D-MobiBrainNet with MRI-DTI data fusion. IEEE Transactions on Medical Robotics and Bionics, 7(1), 42-56.
70. Sait, S. (2024). Multi-class classification of Alzheimer’s disease using a hybrid ResNet152V2 and Inception-Transformer model. Journal of Real-Time Image Processing, 21(3), 88-105. https://doi.org/10.1007/s11554-024-01442-y
71. Sarraf, S., DeSouza, D. D., Anderson, J., Tofighi, G., & ADNI. (2017). DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv, 070441. https://doi.org/ 10.1101/070441
72. Shi, J., Zheng, X., Li, Y., Zhang, Q., & Ying, S. (2018). Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks. IEEE Journal of Biomedical and Health Informatics, 22(1), 173-183. https://doi.org/10.1109/JBHI.2017.2655720
73. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd ICLR, 1-14.
74. Srivastava, S., Ahmed, R., & Khare, S. K. (2021). Alzheimer hastalığı ve farklı yaklaşımlarla tedavisi: Bir gözden geçirme. European Journal of Medicinal Chemistry, 216, 113320. https://doi.org/10.1016/j.ejmech. 2021.113320
75. Stoleru, G. I., & Iftene, A. (2023). Transfer learning for Alzheimer’s disease diagnosis from MRI slices: a comparative study of deep learning models. Procedia Computer Science,225, 2614-2623. https://doi.org/10. 1016/j.procs.2023.10.253
76. Suk, H. I., & Shen, D. (2013). Deep learning-based feature representation for AD/MCI classification. MICCAI 2013, 583-590. https://doi.org/10. 1007/978-3-642-40763-5_72
77. Suk, H. I., Lee, S. W., & Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569-582. https://doi.org/10.1016/j.neuroimage.2014.06.077
78. Suk, H. I., Lee, S. W., & Shen, D. (2015). Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function, 220(2), 841-859. https://doi.org/10.1007/s00429-013-0687-3
79. Suk, H. I., Lee, S. W., & Shen, D. (2017). Deep ensemble learning of sparse regression models for brain disease diagnosis. Medical Image Analysis, 37, 101-113. https://doi.org/10.1016/j.media.2017.01.008
80. Suk, H. I., Lee, S. W., Shen, D., & ADNI. (2016a). Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Structure and Function, 221(5), 2569-2587. https://doi.org/10.1007/s00429-015-1059-z
81. Suk, H. I., Wee, C. Y., Lee, S. W., & Shen, D. (2016b). State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage, 129, 292-307. https://doi.org/10.1016/j.neuroimage. 2016.01.005
82. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, Inception-ResNet and the impact of residual connections on learning. Thirty-First AAAI Conference, 4278-4284. https://doi.org/10.1609/aaai.v31i1.11231
83. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE CVPR, 1-9. https://doi.org/10.1109/CVPR.2015.7298594
84. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE CVPR, 2818-2826. https://doi.org/10.1109/CVPR.2016.308
85. Ungar, L., Altmann, A., & Greicius, M. D. (2014). Apolipoprotein E, gender, and Alzheimer’s disease: An overlooked, but potent and promising interaction. Brain Imaging and Behavior, 8(2), 262-273. https://doi.org/10.1007/s11682-013-9272-x
86. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
87. Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X., ... & ADNI. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of MCI and Alzheimer’s disease. Neurocomputing, 333, 145-156. https://doi.org/10.1016/j.neucom.2018.12.018
88. Wang, S. H., Phillips, P., Sui, Y., Liu, B., Yang, M., & Cheng, H. (2018). Classification of Alzheimer’s disease based on eight-layer CNN with LReLU and max pooling. Journal of Medical Systems, 42(5), 85. https://doi.org/10.1007/s10916-018-0932-7
89. Wang, S., Wang, H., Shen, Y., & Wang, X. (2018). Automatic recognition of MCI and Alzheimer’s disease using ensemble based 3D DenseNet. 2018 17th IEEE ICMLA, 517-523. https://doi.org/10.1109/ICMLA.2018.00084
90. Wen, J., Thibeau-Sutre, E., & Diaz-Melo, M. (2020). Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Medical Image Analysis, 63, 101694. https://doi.org/10.1016/j.media.2020.101694
91. Yang, Z., & Liu, Z. (2020). The risk prediction of Alzheimer’s disease based on the deep learning model of brain 18F-FDG PET. Saudi Journal of Biological Sciences, 27(2), 659-665. https://doi.org/10.1016/j.sjbs.2019. 12.004
92. Zhang, J., Zheng, B., Gao, A., Feng, X., Liang, D., & Long, X. (2021). A 3D densely connected convolution neural network with connection-wise attention mechanism. Magnetic Resonance Imaging, 78, 119-126. https://doi.org/10.1016/j.mri.2021.02.001
93. Zhang, J., Zheng, B., Gao, A., Feng, X., Liang, D., & Long, X. (2021). A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer’s disease classification. Magnetic Resonance Imaging, 78, 119-126. https://doi.org/10.1016/j.mri. 2021.02.001
94. Zhao, X., Ang, C. K. E., Acharya, U. R., & Cheong, K. H. (2021). Application of AI techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybernetics and Biomedical Engineering, 41(2), 456-473. https://doi.org/10.1016/j.bbe.2021.03.004
95. Zhao, Z., Chuah, J. H., Lai, K. W., Chow, C. O., Gochoo, M., Dhanalakshmi, S., ... & Wu, X. (2023). Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: a review. Frontiers in Computational Neuroscience, 17, 1038153. https://doi.org/10.3389/fncom.2023.1038153
96. Zhou, L., Wang, S. H., & Zhang, Y. D. (2022). Alzheimer’s disease identification via deep learning: a review. International Journal of Imaging Systems and Technology, 32(4), 1145-1165. https://doi.org/10. 1002/ima.22723
Volume 4, Issue 1, 2026
Page : 34-45
_Footer