Klasifikasi Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak

Authors

  • Ramanda md Universitas Multi Data Palembang, Indonesia
  • Ery Hartati Universitas Multi Data Palembang, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v5i2.15122

Keywords:

Alzheimer, Convolutional Neural Network (CNN), Classification, SMOTE, VGG19

Abstract

Early detection of Alzheimer's disease is crucial for effective treatment, and the use of brain MRI images has become a common method for diagnosis. However, many previous studies have faced challenges in addressing class imbalance in their datasets, leading to lower accuracy for minority classes. This study aims to address this issue by using a pretrained CNN architecture, VGG19, combined with the SMOTE method to address class integration and improve classification accuracy. This study contributes by introducing SMOTE to the Alzheimer's MRI image dataset to achieve a more balanced class distribution, which has not been fully explored in previous studies. The evaluation results show that the classification accuracy reaches 95%, higher than previous studies using VGG-19 with an accuracy of 77.66%. These results confirm that the use of VGG19 with SMOTE produces better performance, especially in addressing class representation, which is a key contribution of this study. This research has the potential to be applied in more efficient and accurate automated image-based detection systems, especially for the early diagnosis of Alzheimer's disease.

Downloads

Download data is not yet available.

References

Ajagbe, S. A., Amuda, K. A., Oladipupo, M. A., & Afe, O. F. (2021). Multi-classification of alzheimer disease on magnetic resonance images ( MRI ) using deep convolutional neural network ( DCNN ) approaches Multi-classification of alzheimer disease on magnetic resonance images ( MRI ) using deep convolutional neural network ( DCNN ) approaches. April. https://doi.org/10.19101/IJACR.2021.1152001

Alghamedy, F. H., Shafiq, M., Liu, L., Yasin, A., Khan, R. A., & Mohammed, H. S. (2022). Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9211477

Austin, Y. S., Irfano, H., Christopher, J. Y., Sukma, L. C., Putra, O. P., Ardhanto, R. I., & Yudistira, N. (2024). Klasifikasi penyakit Alzheimer dari scan MRI Otak menggunakan Convest Classification of Alzheimer’s Disease from brain MRI Scans using. 11(6), 1223–1232. https://doi.org/10.25126/jtiik.2024118117

Breijyeh, Z., & Karaman, R. (2020). Comprehensive Review on Alzheimer’s Disease: Causes and Treatment. Molecules, 25(24). https://doi.org/10.3390/MOLECULES25245789

Dewi, B., Muflikhah, L., & Setiawan, B. D. (2025). Klasifikasi Penyakit Alzheimer Berdasarkan Citra MRI Otak. 9(6), 1–10.

Fu’Adah, Y. N., Wijayanto, I., Pratiwi, N. K. C., Taliningsih, F. F., Rizal, S., & Pramudito, M. A. (2021). Automated Classification of Alzheimer’s Disease Based on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet Architecture. Journal of Physics: Conference Series, 1844(1). https://doi.org/10.1088/1742-6596/1844/1/012020

Hafidzah, P., Maryani, S., Ihsani, B. Y., Erwin, E., & Niswariyana, A. K. (2024). Penerapan Deep Learning dalam Menganalisis Sentimen di Media Sosial. Seminar Nasional Paedagoria, 4, 328–339.

Huang, J., Chai, J., & Cho, S. (2020). Deep Learning In Finance and Banking. Frontiers of Business Research in China, 14(1).

Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022). Deep learning for Alzheimer's disease diagnosis: A survey. Artificial intelligence in medicine, 130, 102332. https://doi.org/10.1016/j.artmed.2022.102332

Downloads

Published

30-12-2025

How to Cite

md, R., & Hartati, E. (2025). Klasifikasi Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak. Arcitech: Journal of Computer Science and Artificial Intelligence, 5(2), 243–264. https://doi.org/10.29240/arcitech.v5i2.15122

Issue

Vol. 5 No. 2 (2025): December 2025

Section

Articles

Citation Check

License

Copyright (c) 2025 Ramanda md, Ery Hartati

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors who publish with Arcitech: Journal of Computer science and Artificial Intelligence agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).