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(Universitas Amikom Yogyakarta, Indonesia)(2) Kusrini Kusrini
(Universitas Amikom Yogyakarta, Indonesia)*corresponding author
AbstractAlzheimer's disease is a form of progressive dementia that significantly impacts the quality of life of patients and their families. Early detection based on Magnetic Resonance Imaging (MRI) can support faster and more accurate diagnosis, but manual classification requires high expertise and is subjective. This study aims to develop an Alzheimer's MRI image classification model using a Convolutional Neural Network (CNN) based on ResNet18 with transfer learning to classify data into four categories: Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The MRI dataset was processed through pre-processing involving 128×128 grayscale conversion, pixel intensity normalization, and class balancing using class weighting. The model was trained using the Adam optimizer (lr=0.0001) with Early Stopping (patience=7) over 50 epochs. Evaluation using the validation set showed that the model achieved high accuracy for the Non-Demented class. The result indicates that ResNet18 with transfer learning can achieve an accuracy of 94.4%, making this model an effective approach for medium-scale classification of Alzheimer's MRI images.
KeywordsBusiness Intelligence
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DOIhttps://doi.org/10.29099/ijair.v9i2.1504 |
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References
R. A. Hazarika, D. Kandar, and A. K. Maji, “An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 8576–8598, Nov. 2022, doi: 10.1016/j.jksuci.2021.09.003.
A. B. Quintes Steiner, A. F. Jacinto, V. F. D. S. Mayoral, S. M. D. Brucki, and V. D. A. Citero, “Mild cognitive impairment and progression to dementia of Alzheimer’s disease,” Jul. 01, 2017, Associacao Medica Brasileira. doi: 10.1590/1806-9282.63.07.651.
N. An, H. Ding, J. Yang, R. Au, and T. F. A. Ang, “Deep ensemble learning for Alzheimer’s disease classification,” J Biomed Inform, vol. 105, May 2020, doi: 10.1016/j.jbi.2020.103411.
N. Amoroso, S. Quarto, M. La Rocca, S. Tangaro, A. Monaco, and R. Bellotti, “An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer’s disease,” Front Aging Neurosci, vol. 15, 2023, doi: 10.3389/fnagi.2023.1238065.
K. Blennow, H. Zetterberg, and A. M. Fagan, “Fluid biomarkers in Alzheimer disease,” Cold Spring Harb Perspect Med, vol. 2, no. 9, 2012, doi: 10.1101/cshperspect.a006221.
“2019 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 15, no. 3, pp. 321–387, Mar. 2019, doi: 10.1016/j.jalz.2019.01.010.
A. Alberdi, A. Aztiria, and A. Basarab, “On the early diagnosis of Alzheimer’s Disease from multimodal signals: A survey,” 2016, Elsevier B.V. doi: 10.1016/j.artmed.2016.06.003.
E. McDade et al., “Lecanemab in patients with early Alzheimer’s disease: detailed results on biomarker, cognitive, and clinical effects from the randomized and open-label extension of the phase 2 proof-of-concept study,” Alzheimers Res Ther, vol. 14, no. 1, Dec. 2022, doi: 10.1186/s13195-022-01124-2.
B. Zhao, H. Lu, S. Chen, J. Liu, and D. Wu, “Convolutional neural networks for time series classification,” Journal of Systems Engineering and Electronics, vol. 28, no. 1, pp. 162–169, Feb. 2017, doi: 10.21629/JSEE.2017.01.18.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” May 27, 2015, Nature Publishing Group. doi: 10.1038/nature14539.
J. Schmidhuber, “Deep Learning in neural networks: An overview,” Jan. 01, 2015, Elsevier Ltd. doi: 10.1016/j.neunet.2014.09.003.
W. Rawat and Z. Wang, “Deep convolutional neural networks for image classification: A comprehensive review,” Sep. 01, 2017, MIT Press Journals. doi: 10.1162/NECO_a_00990.
A. H. Rashid, A. Gupta, J. Gupta, and M. Tanveer, “Biceph-Net: A Robust and Lightweight Framework for the Diagnosis of Alzheimer’s Disease Using 2D-MRI Scans and Deep Similarity Learning,” IEEE J Biomed Health Inform, vol. 27, no. 3, pp. 1205–1213, Mar. 2023, doi: 10.1109/JBHI.2022.3174033.
N. Hassan, A. S. M. Miah, T. Suzuki, and J. Shin, “Gradual Variation-Based Dual-Stream Deep Learning for Spatial Feature Enhancement With Dimensionality Reduction in Early Alzheimer’s Disease Detection,” IEEE Access, vol. 13, pp. 31701–31717, 2025, doi: 10.1109/ACCESS.2025.3542458.
R. A. Hazarika, A. K. Maji, S. N. Sur, B. S. Paul, and D. Kandar, “A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques,” 2021, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2021.3072559.
M. Pafitis, M. Constantinou, and C. Christodoulou, “Accelerating Training of Convolutional Neural Networks with Hessian-Free Optimization for Detecting Alzheimer’s Disease in Brain MRI,” IEEE Access, vol. 12, pp. 176184–176198, 2024, doi: 10.1109/ACCESS.2024.3487114.
M. Ashtari-Majlan, A. Seifi, and M. M. Dehshibi, “A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer’s Disease Using Structural MRI Images,” IEEE J Biomed Health Inform, vol. 26, no. 8, pp. 3918–3926, Aug. 2022, doi: 10.1109/JBHI.2022.3155705.
Anjali, D. Singh, O. J. Pandey, and H. N. Dai, “STCNN: Combining SMOTE-TOMEK with CNN for Imbalanced Classification of Alzheimer’s Disease,” IEEE Sens Lett, vol. 8, no. 3, pp. 1–4, Mar. 2024, doi: 10.1109/LSENS.2024.3357196.
C. M. Kim and W. Lee, “Classification of Alzheimer’s Disease Using Ensemble Convolutional Neural Network With LFA Algorithm,” IEEE Access, vol. 11, pp. 143004–143015, 2023, doi: 10.1109/ACCESS.2023.3342917.
B. S. Rao and M. Aparna, “A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques,” IEEE Access, vol. 11, pp. 71542–71556, 2023, doi: 10.1109/ACCESS.2023.3294981.
M. Gao, P. Song, F. Wang, J. Liu, A. Mandelis, and D. Qi, “A Novel Deep Convolutional Neural Network Based on ResNet-18 and Transfer Learning for Detection of Wood Knot Defects,” J Sens, vol. 2021, 2021, doi: 10.1155/2021/4428964.
A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” Feb. 2019, [Online]. Available: http://arxiv.org/abs/1803.08375
K. Kumar, I. Suwalka, A. Uche-Ezennia, C. Iwendi, and C. N. Biamba, “An Improved Deep Learning Unsupervised Approach for MRI Tissue Segmentation for Alzheimer’s Disease Detection,” IEEE Access, vol. 12, pp. 188114–188121, 2024, doi: 10.1109/ACCESS.2024.3510454.
B. Dewi, L. Muflikhah, and B. D. Setiawan, “KLASIFIKASI PENYAKIT ALZHEIMER BERDASARKAN CITRA MRI OTAK MENGGUNAKAN RESNET50V2,” 2025. [Online]. Available: http://j-ptiik.ub.ac.id
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