Application of Machine Learning for Dementia Classification through MRI Images using Vertex AI on Google Cloud Services
Abstract
Alzheimer's dementia remains a serious global health challenge, particularly in resource-limited countries where early and accurate diagnosis is crucial to reducing morbidity and mortality rates. Despite advances in medical imaging and diagnostic tools, early detection of Alzheimer’s remains a complex and resource-intensive task for healthcare systems worldwide. This study leverages the power of machine learning, specifically Convolutional Neural Networks (CNN), to develop a reliable model for detecting the severity of dementia using brain MRI images. The dataset used consists of four main categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented, with a total of 1,561 images obtained from Kaggle. The model was trained using Vertex AI on Google Cloud, which automatically optimized model parameters through AutoML and Hyperparameter Tuning. Techniques such as image segmentation and feature extraction were applied to enhance model accuracy. The results show that this CNN model achieved a precision rate of 93.5% for the Non-Demented category, with classification accuracy consistently between 92% and 93% for various other levels of dementia severity. These findings underscore the potential of machine learning, particularly CNN, in significantly improving dementia detection accuracy even in resource-constrained settings. By utilizing advanced techniques such as image segmentation, feature extraction, and CNN-based automated classification, this model offers a promising solution for real-time dementia diagnosis. The scalability and adaptability of the model built using Vertex AI allow for broader applications in global clinical scenarios, supporting public health efforts to reduce the burden of Alzheimer's disease. While challenges regarding data sensitivity and computational resources are acknowledged, the model’s potential to improve early diagnosis and patient outcomes is highly significant.
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References
A. G. M. Sianturi, “Stadium, Diagnosis, dan Tatalaksana Penyakit Alzheimer,” Maj. Kesehat. Indones., vol. 2, no. 2, hal. 39–44, 2021, doi: 10.47679/makein.202132.
I. Zulhaini, “Gangguan Demensia Tipe Alzheimer Pada Lanjut Usia Yang Berdzikir Dengan Yang Tidak Berdzikir,” Univ. Medan Area, hal. 142–150, 2012.
L. Hewes, “The state of the art of dementia research: New frontiers,” Prof. Geogr., vol. 2, no. 4, hal. 14–20, 2018, doi: 10.1111/j.0033-0124.1950.24_14.x.
N. Yamanakkanavar, J. Y. Choi, dan B. Lee, “MRI segmentation and classification of human brain using deep learning for diagnosis of alzheimer’s disease: A survey,” Sensors (Switzerland), vol. 20, no. 11, hal. 1–31, 2020, doi: 10.3390/s20113243.
M. F. Nazil, A. B. Firmansyah, dan R. Purbaningtyas, “Klasifikasi Keparahan Demensia Alzheimer Menggunakan Metode Convolutional Neural Network pada Citra MRI Otak,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, hal. 1–7, 2023, doi: 10.57152/malcom.v3i1.200.
F. Akbar dan Rahmaddeni, “Komparasi Algoritma Machine Learning Untuk Memprediksi Penyakit Alzheimer,” J. Komput. Terap., vol. 8, no. 2, hal. 236–245, 2022, Tersedia pada: https://jurnal.pcr.ac.id/index.php/jkt/.
S. E. Sorour, A. A. A. El-Mageed, K. M. Albarrak, A. K. Alnaim, A. A. Wafa, dan E. El-Shafeiy, “Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques,” J. King Saud Univ. - Comput. Inf. Sci., vol. 36, no. 2, hal. 101940, 2024, doi: 10.1016/j.jksuci.2024.101940.
M. A. Wirya, “Deteksi Penyakit Alzheimer Pada Citra Magnetic Resonance Imaging Menggunakan Machine Learning Dengan Metode Convolutional Neural Network Muhammad Adi Wirya Program Studi Fisika 1444 H / 2023 M,” Repository.Uinjkt.Ac.Id, 2023, [Daring]. Tersedia pada: https://repository.uinjkt.ac.id/dspace/handle/123456789/73962%0Ahttps://repository.uinjkt.ac.id/dspace/bitstream/123456789/73962/1/MUHAMMAD ADI WIRYA-FST.pdf.
A. Javeed, A. L. Dallora, J. S. Berglund, A. Ali, L. Ali, dan P. Anderberg, “Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.,” J. Med. Syst., vol. 47, no. 1, hal. 17, Feb 2023, doi: 10.1007/s10916-023-01906-7.
A. Yudhana, Sunardi, dan S. Saifullah, “Kompresi Wavelet Untuk Identifikasi Telur,” Ilk. J. Ilm., vol. 8, no. Desember, hal. 190–196, 2016.
N. D. Nath, T. Chaspari, dan A. H. Behzadan, “Single- And multi-label classification of construction objects using deep transfer learning methods,” J. Inf. Technol. Constr., vol. 24, no. December, hal. 511–526, 2019, doi: 10.36680/J.ITCON.2019.028.
A. chandra Saputra, “Penentuan Parameter Learning Rate Selama Pembelajaran Jaringan Syaraf Tiruan Backpropagation Menggunakan Algoritma Genetika,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 14, no. 2, hal. 202–212, 2020, doi: 10.47111/jti.v14i2.1141.
W. Hidayat, M. Ardiansyah, dan A. Setyanto, “Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb,” Edumatic J. Pendidik. Inform., vol. 5, no. 1, hal. 11–20, 2021, doi: 10.29408/edumatic.v5i1.3125.
P. Romadloni, B. Adhi Kusuma, dan W. Maulana Baihaqi, “Komparasi Metode Pembelajaran Mesin Untuk Implementasi Pengambilan Keputusan Dalam Menentukan Promosi Jabatan Karyawan,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, hal. 622–628, 2022, doi: 10.36040/jati.v6i2.5238.
Praveen Borra, “A Survey of Google Cloud Platform (GCP): Features, Services, and Applications,” Int. J. Adv. Res. Sci. Commun. Technol., no. June, hal. 191–199, 2024, doi: 10.48175/ijarsct-18922.
H. Jeong, “Preliminary Test of Google Vertex Artificial Intelligence in Root Dental X-ray Imaging Diagnosis,” U1 Univ., vol. 18, no. 3, hal. 267–273, 2024.
D. Gustian, Y. Fitrisia, W. Novayani, dan S. Purwantoro E.S.G.S, “Implementasi Automation Deployment pada Google Cloud Compute VM menggunakan Terraform,” INOVTEK Polbeng - Seri Inform., vol. 8, no. 1, hal. 50, 2023, doi: 10.35314/isi.v8i1.3095.
D. Lusita, F. Anissa, dan R. Andryani, “Penerapan Cloud Computing Dalam Aplikasi Panggil Teknisi Berbasis Android Menggunakan Google Cloud Platform,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 2, hal. 1292–1300, 2022.
M. Agil Izzulhaq dan Alamsyah, “Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet50V2 Untuk Mengidentifikasi Penyakit Pneumonia,” Indones. J. Math. Nat. Sci., vol. 47, no. 1, hal. 12–22, 2024, [Daring]. Tersedia pada: https://journal.unnes.ac.id/journals/JM/index.
E. Purnama Sari et al., “Studi Literatur Deep Learning dan Machine Learning untuk Analisis dan Prediksi Pasar Saham: Metodologi, Representasi Data dan Studi Kasus,” J. Teknol. dan Sains Mod., vol. 1, no. 1, hal. 19–28, 2024, [Daring]. Tersedia pada: https://journal.scitechgrup.com/index.php/jtsm.
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