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Enhancing CNN Performance for Alzheimer’s Disease Classification Using Genetic Algorithm Optimization
Corresponding Author(s) : Wildan Arif Maulana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026 (Article in Progress)
Abstract
The increasing global life expectancy has led to a rapidly growing elderly population, resulting in a higher prevalence of Alzheimer’s disease and a pressing need for effective diagnostic solutions. Despite advances in medical imaging, the early and accurate detection of Alzheimer’s disease remains a major challenge due to subtle differences in brain structures across disease stages. However, the interpretation of MRI images still depends heavily on the abilities of individual medical personnel, which risks introducing subjectivity and potential errors in the diagnostic process. In this context, particularly deep learning, emerges as an effective strategy to overcome these limitations by automating the analysis of medical images and reducing human bias. To address this issue, a custom Convolutional Neural Network (CNN) model was developed from scratch for Alzheimer’s disease classification using brain MRI images. To enhance data diversity and mitigate overfitting, a combination of Albumentations and CutMix data augmentation techniques was applied, yielding an initial classification accuracy of 90%. Model performance was further optimized using a Genetic Algorithm (GA), which efficiently explored the hyperparameter space and identified optimal configurations, boosting classification accuracy to 96%. The optimized model demonstrated robust generalization across all disease categories, confirming the effectiveness of the proposed approach. This research contributes to the development of a more reliable and adaptive deep learning framework for early-stage Alzheimer’s disease detection, offering potential support for clinical diagnostic systems
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- A. Nova Setiyanto, A. Nur Mayani, R. Sakit Adi Husada Undaan Wetan Surabaya, and S. Guna Bangsa Yogyakarta, “Aplikasi 3d Arterial Spin Labeling Sequence Pada Pemeriksaan Brain MRI,” 2020.
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- N. Meenakshisundaram and Sajiv. G, “Evaluating Oversampling Strategies for Imbalanced Cervical Cancer Risk Prediction: A Comparative Analysis of SMOTE, Borderline-SMOTE and ADASYN,” in 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), IEEE, Aug. 2025, pp. 1–6. doi: 10.1109/NMITCON65824.2025.11188220.
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- A. Indriani, “Klasifikasi Data Forum dengan menggunakan Metode Naïve Bayes Classifier,” 2014. [Online]. Available: www.bluefame.com,
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- H. M. Yumil, F. Sia, T. S. Fun, and L. P. Hung, “Optimized Convolutional Neural Network Using Genetic Algorithm for Music Genre Classification,” in 6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 547–550. doi: 10.1109/IICAIET62352.2024.10729917.
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References
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A. Calderaro et al., “The Neuroprotective Potentiality of Flavonoids on Alzheimer’s Disease,” Dec. 01, 2022, MDPI. doi: 10.3390/ijms232314835.
A. Nova Setiyanto, A. Nur Mayani, R. Sakit Adi Husada Undaan Wetan Surabaya, and S. Guna Bangsa Yogyakarta, “Aplikasi 3d Arterial Spin Labeling Sequence Pada Pemeriksaan Brain MRI,” 2020.
G. A. Sonn et al., “Prostate Magnetic Resonance Imaging Interpretation Varies Substantially Across Radiologists,” Eur Urol Focus, vol. 5, no. 4, pp. 592–599, Jul. 2019, doi: 10.1016/j.euf.2017.11.010.
A. Stabile et al., “Factors Influencing Variability in the Performance of Multiparametric Magnetic Resonance Imaging in Detecting Clinically Significant Prostate Cancer: A Systematic Literature Review,” Eur Urol Oncol, vol. 3, no. 2, pp. 145–167, Apr. 2020, doi: 10.1016/j.euo.2020.02.005.
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G. Singh, K. Guleria, and S. Sharma, “A Deep Learning-based Convolutional Neural Network Model for Alzheimer’s Disease Detection,” in 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES), IEEE, Nov. 2024, pp. 1–5. doi: 10.1109/IC3TES62412.2024.10877583.
W. Adi Kurniawan and A. Salam, “Penggunaan Feature Space SMOTE Untuk Mengurangi Overfitting Akibat Imbalance Dataset Utilization of Feature Space SMOTE to Reduce Overfitting Due to Imbalanced Dataset,” 2025.
A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, “Albumentations: Fast and flexible image augmentations,” Information (Switzerland), vol. 11, no. 2, Feb. 2020, doi: 10.3390/info11020125.
H. Bumpenje, Rahmadwati, and Z. Abidin, “Chest Cancer Classification from Chest CT-Scan Images using Deep Learning,” in 2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 49–55. doi: 10.1109/SIML61815.2024.10578120.
S. Yun, D. Han, S. Chun, S. J. Oh, J. Choe, and Y. Yoo, “CutMix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., Oct. 2019, pp. 6022–6031. doi: 10.1109/ICCV.2019.00612.
O. Salih and K. J. Duffy, “Optimization Convolutional Neural Network for Automatic Skin Lesion Diagnosis Using a Genetic Algorithm,” Applied Sciences (Switzerland), vol. 13, no. 5, Mar. 2023, doi: 10.3390/app13053248.
K. V. Sridhar, V. K. Tiwari, R. Mounica, and K. Tejaswi, “Brain Tumor Classification Using Enhanced CNN and Optimization with Metaheuristic Algorithms,” in 2024 7th International Conference on Signal Processing and Information Security, ICSPIS 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/ICSPIS63676.2024.10812605.
R. RAHMADWATI, A. Z. IMRAN, M. ASWIN, and K. FERDIANA, “Identifikasi Penyakit Katarak berdasarkan Citra Fundus menggunakan Siamese Convolutional Neural Network,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 12, no. 4, p. 838, Dec. 2024, doi: 10.26760/elkomika.v12i4.838.
N. Meenakshisundaram and Sajiv. G, “Evaluating Oversampling Strategies for Imbalanced Cervical Cancer Risk Prediction: A Comparative Analysis of SMOTE, Borderline-SMOTE and ADASYN,” in 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), IEEE, Aug. 2025, pp. 1–6. doi: 10.1109/NMITCON65824.2025.11188220.
S. Rani, T. Ahmad, and S. Masood, “Handling Class Imbalance Problem using Oversampling Techniques for Breast Cancer Prediction,” in 2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 693–698. doi: 10.1109/REEDCON57544.2023.10150702.
S. Yun, D. Han, S. Chun, S. J. Oh, J. Choe, and Y. Yoo, “CutMix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., Oct. 2019, pp. 6022–6031. doi: 10.1109/ICCV.2019.00612.
A. Indriani, “Klasifikasi Data Forum dengan menggunakan Metode Naïve Bayes Classifier,” 2014. [Online]. Available: www.bluefame.com,
R. Siwi Pradini, M. Anshori, M. Syauqi Haris, I. Teknologi, dan R. Kesehatan dr Soepraoen Kesdam V, and P. Korespondensi, “Optimasi Weight Ahp Menggunakan Genetic Algorithm Untuk Rekomendasi Platform Media Sosial Sebagai Sarana Promosi Digital,” 2024, doi: 10.25126/jtiik2024118011.
T. Listyorini and S. Muzid, “Implementasi Population Resizing On Fitness Improvement Genetic Algorithm (Profiga) Untuk Optimasi Rute Kunjungan Promosi Universitas Muria Kudus Berbasis Android Dan Google Maps API,” Jurnal SIMETRIS, vol. 7, no. 1, 2016.
M. S. . Obaidat, IEEE CCCI 2020 : proceedings of the 2020 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics : November 03-05, 2020. IEEE, 2020.
K. Rajagopal, V. S. Kumari, S. Saraswathy, V. S. Kumar, S. Ponmaniraj, and A. Deepa, “Hybrid Deep Learning Models with Genetic Algorithm Optimization for Enhanced Kidney Tumor Detection,” in 5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 1525–1530. doi: 10.1109/ICESC60852.2024.10689997.
K. V. Sridhar, V. K. Tiwari, R. Mounica, and K. Tejaswi, “Brain Tumor Classification Using Enhanced CNN and Optimization with Metaheuristic Algorithms,” in 2024 7th International Conference on Signal Processing and Information Security, ICSPIS 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/ICSPIS63676.2024.10812605.
H. M. Yumil, F. Sia, T. S. Fun, and L. P. Hung, “Optimized Convolutional Neural Network Using Genetic Algorithm for Music Genre Classification,” in 6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 547–550. doi: 10.1109/IICAIET62352.2024.10729917.
Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, “Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,” IEEE Trans Cybern, vol. 50, no. 9, pp. 3840–3854, Sep. 2020, doi: 10.1109/TCYB.2020.2983860.
F. Y. Santoso, E. Sediyono, and H. D. Purnomo, “Genetic Algorithm For Convolutional Neural Network Hyperparameter Tuning,” in 2023 3rd International Conference on Computing and Information Technology, ICCIT 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 232–236. doi: 10.1109/ICCIT58132.2023.10273928.