Multimodal Deep Learning Framework for Alzheimer’s Disease Classification Using MRI Scans and Clinical Records
DOI:
https://doi.org/10.22399/ijcesen.3672Keywords:
Alzheimer's Disease, Deep Learning, EfficientNet-B7, Multimodal Fusion, MRI, Clinical Data, PCA, Neural NetworksAbstract
Alzheimer's disease (AD) is a neurodegenerative disorder that strikes millions of people globally. Classification that is both accurate and early-stage is key to successful intervention and treatment planning. In this paper, AlzFusionNet, a multimodal deep learning model fusing MRI features with clinical information for enhanced AD classification, is proposed. EfficientNet-B7 is used as the backbone of the model to extract MRI features, and PCA is used to decrease the dimensionality of clinical information. A fusion layer merges both modalities for four-stage classification: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Experimental outcomes prove that AlzFusionNet outperforms single-modality models in terms of accuracy (96.8%). The results underscore the advantages of multimodal integration for AD classification.
References
[1] Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific reports. 2021 Feb 5;11(1):3254.
[2] Lee G, Nho K, Kang B, Sohn KA, Kim D. Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Scientific reports. 2019 Feb 13;9(1):1952.
[3] Golovanevsky M, Eickhoff C, Singh R. Multimodal attention-based deep learning for Alzheimer’s disease diagnosis. Journal of the American Medical Informatics Association. 2022 Dec 1;29(12):2014-22.
[4] Jia H, Lao H. Deep learning and multimodal feature fusion for the aided diagnosis of Alzheimer's disease. Neural Computing and Applications. 2022 Nov;34(22):19585-98.
[5] Hassan A, Imran A, Yasin AU, Waqas MA, Fazal R. A Multimodal Approach for Alzheimer's Disease Detection and Classification Using Deep Learning. Journal of Computing & Biomedical Informatics. 2024 Mar 1;6(02):441-50.
[6] Battineni G, Hossain MA, Chintalapudi N, Traini E, Dhulipalla VR, Ramasamy M, Amenta F. Improved Alzheimer’s disease detection by MRI using multimodal machine learning algorithms. Diagnostics. 2021 Nov 13;11(11):2103.
[7] Houria L, Belkhamsa N, Cherfa A, Cherfa Y. Multi-modality MRI for Alzheimer’s disease detection using deep learning. Physical and Engineering Sciences in Medicine. 2022 Dec;45(4):1043-53.
[8] Khan A, Zubair S. An improved multi-modal based machine learning approach for the prognosis of Alzheimer’s disease. Journal of King Saud University-Computer and Information Sciences. 2022 Jun 1;34(6):2688-706.
[9] Massalimova A, Varol HA. Input agnostic deep learning for Alzheimer’s disease classification using multimodal MRI images. In2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021 Nov 1 (pp. 2875-2878). IEEE.
[10] Jo T, Nho K, Saykin AJ. Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data. Frontiers in aging neuroscience. 2019 Aug 20;11:220.
[11] Abrol A, Fu Z, Du Y, Calhoun VD. Multimodal data fusion of deep learning and dynamic functional connectivity features to predict Alzheimer’s disease progression. In2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) 2019 Jul 23 (pp. 4409-4413). IEEE.
[12] Dwivedi S, Goel T, Tanveer M, Murugan R, Sharma R. Multimodal fusion-based deep learning network for effective diagnosis of Alzheimer’s disease. IEEE MultiMedia. 2022 Mar 7;29(2):45-55.
[13] Zhang F, Li Z, Zhang B, Du H, Wang B, Zhang X. Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease. Neurocomputing. 2019 Oct 7;361:185-95.
[14] Sharma S, Mandal PK. A comprehensive report on machine learning-based early detection of alzheimer's disease using multi-modal neuroimaging data. ACM Computing Surveys (CSUR). 2022 Mar 14;55(2):1-44.
[15] Elazab A, Wang C, Abdelaziz M, Zhang J, Gu J, Gorriz JM, Zhang Y, Chang C. Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions. Expert Systems with Applications. 2024 Jul 14:124780.
[16] Islam J, Zhang Y. A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. InBrain Informatics: International Conference, BI 2017, Beijing, China, November 16-18, 2017, Proceedings 2017 (pp. 213-222). Springer International Publishing.
[17] Patel PJ, Yevle D, Diwan D, Ranga S, Gandhi K, Dumasia S, Nayak R. Performance analysis of deep learning algorithms for classifying chronic obstructive pulmonary disease. Journal of Integrated Science and Technology. 2024;12(2):745-.
[18] Shikalgar A, Sonavane S. Hybrid deep learning approach for classifying Alzheimer disease based on multimodal data. InComputing in Engineering and Technology: Proceedings of ICCET 2019 2020 (pp. 511-520). Springer Singapore.
[19] Lu D, Popuri K, Ding GW, Balachandar R, Beg MF. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Scientific reports. 2018 Apr 9;8(1):5697.
[20] Odusami M, Maskeliūnas R, Damaševičius R, Misra S. Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: A systematic review and meta-analysis. Cognitive Neurodynamics. 2024 Jun;18(3):775-94.
[21] Deshpande S, Kulkarni N. Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature. SN Computer Science. 2024 Sep 2;5(7):852.
[22] Prabhu SS, Berkebile JA, Rajagopalan N, Yao R, Shi W, Giuste F, Zhong Y, Sun J, Wang MD. Multi-Modal Deep Learning Models for Alzheimer's Disease Prediction Using MRI and EHR. In2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE) 2022 Nov 7 (pp. 168-173). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
