MRI Image Analysis for Alzheimer’s Disease Detection Using Transfer Learning: VGGNet vs. EfficientNet

Green Arther Sandag; Eleonora Djamal; George Morris William Tangka; Semmy Wellem Taju

doi:10.31154/cogito.v10i2.836.580-592

Authors

Green Arther Sandag Universitas Klabat https://orcid.org/0000-0002-0622-801X
Eleonora Djamal Universitas Klabat
George Morris William Tangka Universitas Klabat
Semmy Wellem Taju Universitas Klabat

DOI:

https://doi.org/10.31154/cogito.v10i2.836.580-592

Keywords:

Alzheimer’s, Transfer Learning, Deep Learning, VGG, EfficientNet

Abstract

This study focuses on developing an effective Alzheimer's disease (AD) classification model using MRI images and transfer learning. This research targets individuals aged 65 and above who are affected by the predominant form of dementia and utilizes an Alzheimer's Disease MRI Image dataset from Kaggle. Model selection involved options like EfficientNetB1, B3, B5, B7, VGG16, and VGG19. Two scenarios with distinct batch sizes (10 and 20) were explored in the model creation process. Evaluation, using a confusion matrix, determined that the EfficientNetB5 model yielded the highest accuracy at 99.22%, surpassing other models such as EfficientNetB1, B3, B7, VGG16, and VGG19. Notably, this research highlights the superior performance of EfficientNet over VGGNet in transfer learning for analyzing Alzheimer's disease MRI images. The study concludes with the implementation of a simple web system for testing model outcomes. Overall, the investigation underscores the efficacy of Convolutional Neural Network (CNN) modeling in Alzheimer's disease analysis and identifies EfficientNetB5 as the optimal model for accurate classification.

Author Biography

Green Arther Sandag, Universitas Klabat

Program Studi Teknik Informatika

References

Alzheimers Disease Current and Future Perspectives. OMICS International, 2016. doi: 10.4172/978-1-63278-067-6-68.

S. E. Martina, “Caregiver Training on Care for People With Dementia in Medan, North Sumatera,” Darmabakti Cendekia J. Community Serv. Engag., vol. 2, no. 1, p. 1, 2020, doi: 10.20473/dc.v2.i1.2020.1-3.

M. Leela, K. Helenprabha, and L. Sharmila, “Prediction and classification of Alzheimer Disease categories using Integrated Deep Transfer Learning Approach,” Meas. Sensors, vol. 27, Jun. 2023, doi: 10.1016/j.measen.2023.100749.

“World Alzheimer Report 2021: Journey through the diagnosis of dementia.”

N. M. Khan, N. Abraham, and M. Hon, “Transfer Learning with Intelligent Training Data Selection for Prediction of Alzheimer’s Disease,” IEEE Access, vol. 7, pp. 72726–72735, 2019, doi: 10.1109/ACCESS.2019.2920448.

P. A. Miceli, W. D. Blair, and M. M. Brown, Isolating Random and Bias Covariances in Tracks. 2018. doi: 10.23919/ICIF.2018.8455530.

S. Sidharth, A. A. Samuel, R. H, J. T. Panachakel, and S. P. K, “Emotion Detection from EEG using Transfer Learning,” in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul. 2023, pp. 1–4. doi: 10.1109/EMBC40787.2023.10340389.

R. Gupta, K. K. Bhardwaj, and D. K. Sharma, “Transfer Learning,” Mach. Learn. Big Data Concepts, Algorithms, Tools Appl., pp. 337–360, 2020, doi: 10.1002/9781119654834.ch13.

H. Acharya, R. Mehta, and D. Kumar Singh, “Alzheimer Disease Classification Using Transfer Learning,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, no. Iccmc, pp. 1503–1508, 2021, doi: 10.1109/ICCMC51019.2021.9418294.

N. Hon, Marcia & Khan, Toward’s Alzheimer’s Disease Classification through Transfer Learning. IEEE, 2017.

A. Ebrahimi, S. Luo, and R. Chiong, “Introducing Transfer Leaming to 3D ResNet-18 for Alzheimer’s Disease Detection on MRI Images,” Int. Conf. Image Vis. Comput. New Zeal., vol. 2020-Novem, 2020, doi: 10.1109/IVCNZ51579.2020.9290616.

W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, “Feature Extraction Methods: A Review,” J. Phys. Conf. Ser., vol. 1591, no. 1, 2020, doi: 10.1088/1742-6596/1591/1/012028.

A. Shamila Ebenezer, S. Deepa Kanmani, M. Sivakumar, and S. Jeba Priya, “Effect of image transformation on EfficientNet model for COVID-19 CT image classification,” Mater. Today Proc., vol. 51, pp. 2512–2519, 2022, doi: 10.1016/j.matpr.2021.12.121.

M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” presented at the International conference on machine learning, PMLR, 2019, pp. 6105–6114.

Z. Liu, J. John, and E. Agu, “Diabetic foot ulcer ischemia and infection classification using efficientnet deep learning models,” IEEE Open J. Eng. Med. Biol., vol. 3, pp. 189–201, 2022.

Z. Cao, J. Huang, X. He, and Z. Zong, “BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images,” Knowledge-Based Syst., vol. 258, p. 110040, 2022, doi: 10.1016/j.knosys.2022.110040.

X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif. Intell. Rev., vol. 57, no. 4, p. 99, 2024.

R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595.

M. Zaabi, N. Smaoui, H. Derbel, and W. Hariri, “Alzheimer’s disease detection using convolutional neural networks and transfer learning based methods,” in Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, Institute of Electrical and Electronics Engineers Inc., Jul. 2020, pp. 939–943. doi: 10.1109/SSD49366.2020.9364155.

M. T. Abed, U. Fatema, S. A. Nabil, M. A. Alam, and M. T. Reza, “Alzheimer’s Disease Prediction Using Convolutional Neural Network Models Leveraging Pre-existing Architecture and Transfer Learning,” 2020 Jt. 9th Int. Conf. Informatics, Electron. Vis. 2020 4th Int. Conf. Imaging, Vis. Pattern Recognition, ICIEV icIVPR 2020, 2020, doi: 10.1109/ICIEVicIVPR48672.2020.9306649.