Improving Alzheimer’s Disease Detection Based Deep Learning and Magnetic Resonance Imaging

Hakar Hassan; Naaman Omar Yaseen

doi:10.65542/djei.v2i3.62

Authors

Hakar Hassan Ararat Private Technical Institute, Duhok, Kurdistan Region, Iraq.
Naaman Omar Yaseen Dohuk Polytechnic University, Amedi Technical Institute, Information Technology Department, Duhok, Kurdistan Region, Iraq https://orcid.org/0000-0001-6513-1640

DOI:

https://doi.org/10.65542/djei.v2i3.62

Keywords:

Alzheimer’s disease;, Deep Learning (DL);, Magnetic Resonance Imaging (MRI);, ResNet50;, Classifier;

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disease that severely affects cognitive functions, which results in a serious memory impairment accompanied by personality changes. An active examination, which is sensitive and effective on time, is a must. Cerebrospinal fluid (CSF), Magnetic Resonance Imaging (MRI), and positron emission tomography (PET) are conventional methods for diagnosing the disease that are also the most invasive, expensive, and require special knowledge. Artificial intelligence (AI) and deep learning (DL) represent one of the key areas in which these advancements have been realized. Neuroimaging is a rapidly evolving field that has played a pivotal role in the earlier diagnosis of AD. As the name implies, the objective of the research is to come up with an advanced learning model based on ResNet50 for the diagnosis of AD. For this reason, advanced preprocessing techniques as well as a data augmentation approach have been employed to address this problem. The proposed ResNet50 model achieved 99.2% test accuracy, 99% precision, 99% recall, and 99% F1-score, demonstrating its superior performance compared with the other evaluated models. The results of the experimental evaluations show the fact that the classification has a high level of accuracy which in turn confirms the reliability of the AI-perceived models in the early-stage identification of AD. Nevertheless, issues like data access, computational costs, and clinical embedding need to be dealt with in order to promote ubiquitous use. The outcomes stress the possibility of AI-generated tools to usher in a new era of Alzheimer's diagnostics.

References

1. Pan, D.; Zeng, A.; Jia, L.; Huang, Y.; Frizzell, T.; Song, X. Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning. Front. Neurosci. 2020, 14, doi:10.3389/fnins.2020.00259.

2. Chen, Y.; Wang, L.; Ding, B.; Shi, J.; Wen, T.; Huang, J.; Ye, Y. Automated Alzheimer’s Disease Classification Using Deep Learning Models with Soft-NMS and Improved ResNet50 Integration. J. Radiat. Res. Appl. Sci. 2024, 17, 100782, doi:10.1016/j.jrras.2023.100782.

3. Nguyen, D.; Nguyen, H.; Ong, H.; Le, H.; Ha, H.; Duc, N.T.; Ngo, H.T. Ensemble Learning Using Traditional Machine Learning and Deep Neural Network for Diagnosis of Alzheimer’s Disease. IBRO Neurosci. Rep. 2022, 13, 255–263, doi:10.1016/j.ibneur.2022.08.010.

4. Proceedings, International Conference on Smart Electronics and Communication (ICOSEC 2020) : 10-12, September 2020; IEEE, 2020; ISBN 9781728154619.

5. Khagi, B.; Kwon, G.R. 3D CNN Design for the Classification of Alzheimer’s Disease Using Brain MRI and PET. IEEE Access 2020, 8, 217830–217847, doi:10.1109/ACCESS.2020.3040486.

6. Altwijri, O.; Alanazi, R.; Aleid, A.; Alhussaini, K.; Aloqalaa, Z.; Almijalli, M.; Saad, A. Novel Deep-Learning Approach for Automatic Diagnosis of Alzheimer’s Disease from MRI. Applied Sciences (Switzerland) 2023, 13, doi:10.3390/app132413051.

7. Fulton, L. V.; Dolezel, D.; Harrop, J.; Yan, Y.; Fulton, C.P. Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and Resnet-50. Brain Sci. 2019, 9, doi:10.3390/brainsci9090212.

8. El-Assy, A.M.; Amer, H.M.; Ibrahim, H.M.; Mohamed, M.A. A Novel CNN Architecture for Accurate Early Detection and Classification of Alzheimer’s Disease Using MRI Data. Sci. Rep. 2024, 14, doi:10.1038/s41598-024-53733-6.

9. Nithya, V.P.; Mohanasundaram, N.; Santhosh, R. An Early Detection and Classification of Alzheimer’s Disease Framework Based on ResNet-50. Current Medical Imaging Formerly Current Medical Imaging Reviews 2023, 20, doi:10.2174/1573405620666230825113344.

10. Pallawi, S.; Singh, D.K. Review and Analysis of Deep Neural Network Models for Alzheimer’s Disease Classification Using Brain Medical Resonance Imaging. Cognitive Computation and Systems 2023, 5, 1–13.

11. Ebrahimi, A.; Luo, S.; Chiong, R. Deep Sequence Modelling for Alzheimer’s Disease Detection Using MRI. Comput. Biol. Med. 2021, 134, doi:10.1016/j.compbiomed.2021.104537.

12. Pandey, P.K.; Pruthi, J.; Alzahrani, S.; Verma, A.; Zohra, B. Enhancing Healthcare Recommendation: Transfer Learning in Deep Convolutional Neural Networks for Alzheimer Disease Detection. Front. Med. (Lausanne). 2024, 11, doi:10.3389/fmed.2024.1445325.

13. Asaduzzaman, M.; Alom, M.K.; Karim, M.E. ALZENET: Deep Learning-Based Early Prediction of Alzheimer’s Disease through Magnetic Resonance Imaging Analysis. Telematics and Informatics Reports 2025, 17, doi:10.1016/j.teler.2025.100189.

14. Yildirim, M.; Cinar, A. Classification of Alzheimer’s Disease MRI Images with CNN Based Hybrid Method. Ingenierie des Systemes d’Information 2020, 25, 413–418, doi:10.18280/isi.250402.

15. EL-Geneedy, M.; Moustafa, H.E.D.; Khalifa, F.; Khater, H.; AbdElhalim, E. An MRI-Based Deep Learning Approach for Accurate Detection of Alzheimer’s Disease. Alexandria Engineering Journal 2023, 63, 211–221, doi:10.1016/j.aej.2022.07.062.

16. Fareed, M.M.S.; Zikria, S.; Ahmed, G.; Mui-Zzud-Din; Mahmood, S.; Aslam, M.; Jillani, S.F.; Moustafa, A.; Asad, M. ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans. IEEE Access 2022, 10, 96930–96951, doi:10.1109/ACCESS.2022.3204395.

17. Chabib, C.M.; Hadjileontiadis, L.J.; Shehhi, A. Al DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease. IEEE Access 2023, 11, 44650–44659, doi:10.1109/ACCESS.2023.3272482.

18. Ajagbe, S.A.; Amuda, K.A.; Oladipupo, M.A.; AFE, O.F.; Okesola, K.I. Multi-Classification of Alzheimer Disease on Magnetic Resonance Images (MRI) Using Deep Convolutional Neural Network (DCNN) Approaches. International Journal of Advanced Computer Research 2021, 11, 51–60, doi:10.19101/ijacr.2021.1152001.

19. Fu’Adah, Y.N.; Wijayanto, I.; Pratiwi, N.K.C.; Taliningsih, F.F.; Rizal, S.; Pramudito, M.A. Automated Classification of Alzheimer’s Disease Based on MRI Image Processing Using Convolutional Neural Network (CNN) with AlexNet Architecture. In Proceedings of the Journal of Physics: Conference Series; IOP Publishing Ltd, March 22 2021; Vol. 1844.

20. Alsubaiea, M.G.; Luo, S.; Shaukat, K. ConvADD: Exploring a Novel CNN Architecture for Alzheimer’s Disease Detection. International Journal of Advanced Computer Science and Applications 2024, 15, 300–313, doi:10.14569/IJACSA.2024.0150431.

21. Saturi, S.; Adiba, R.; I, Y.R.; Md, A.S.; D, P. Advanced MRI-Based Alzheimer’s Disease Classification with Hybrid Convolutional Neural Networks. Journal of Computer Allied Intelligence 2025, 3, 31–39, doi:10.69996/jcai.2025003.

22. Nithiyanandam, N.; Mohideen, P.; Vishnuvardhan, V.; Madhuri, D. INVESTIGATION ON EARLY RECOGNITION OF ALZHEIMER’S AILMENT BY MEANS OF DIFFERENT KINDS OF NEURAL NETWORK ARCHITECTURE 1. 13, 2025.

23. Alshammari, M.; Mezher, M. A Modified Convolutional Neural Networks for MRI-Based Images for Detection and Stage Classification of Alzheimer Disease. In Proceedings of the Proceedings - 2021 IEEE 4th National Computing Colleges Conference, NCCC 2021; Institute of Electrical and Electronics Engineers Inc., March 27 2021.

24. Mohammed, B.A.; Senan, E.M.; Rassem, T.H.; Makbol, N.M.; Alanazi, A.A.; Al-Mekhlafi, Z.G.; Almurayziq, T.S.; Ghaleb, F.A. 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 (Switzerland) 2021, 10, doi:10.3390/electronics10222860.

25. Khalid, A.; Senan, E.M.; Al-Wagih, K.; Ali Al-Azzam, M.M.; Alkhraisha, Z.M. Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted Features. Diagnostics 2023, 13, doi:10.3390/diagnostics13091654.