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Komparasi Performa Model 3D CNN dalam Klasifikasi Demensia Alzheimer pada MRI Otak
https://doi.org/10.22146/ijeis.103041 Auriel Azril Ardin(1), Dyah Aruming Tyas(2*)
(1) Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(2) Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
Penyakit Alzheimer adalah jenis demensia akibat kerusakan pada neuron otak yang memengaruhi memori, bahasa, dan berpikir. Diagnosis manual sering rentan terhadap subjektivitas dan memakan waktu, sehingga diperlukan model otomatis seperti 3D CNN untuk klasifikasi tingkat keparahan Alzheimer. Namun, kompleksitas arsitektur 3D CNN menyebabkan waktu komputasi yang tinggi. Penelitian ini membandingkan tiga arsitektur model 3D CNN yaitu 3D ResNet, 3D ResNeXt + Bi-LSTM, dan 3D CNN + CLSTM untuk menentukan model yang optimal. Dataset yang digunakan diperoleh dari database ADNI. Performa model dievaluasi dengan menggunakan confusion matrix, akurasi, presisi, recall, F1-score dan waktu komputasi.
Hasil penelitian menunjukkan bahwa 3D ResNet memiliki akurasi pelatihan tertinggi mencapai 99,54% dan waktu komputasi pelatihan sebesar 57,61 detik/epoch. Model 3D ResNeXt + Bi-LSTM mencapai akurasi pengujian sebesar 99,33% dan waktu inferensi tercepat yaitu 0,0182 detik/sampel, namun waktu komputasi pelatihan terlama yaitu 117,68 detik/epoch. Sementara itu, 3D CNN + CLSTM mencapai akurasi uji sempurna 100% tetapi memiliki waktu inferensi terlama yaitu 0,0268 detik/sampel. Penelitian ini menunjukkan bahwa arsitektur yang sederhana tetap dapat memberikan performa optimal dengan waktu komputasi yang lebih efisien dibandingkan model yang lebih kompleks.
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Alzheimer’s Association., “2023 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 19, no. 4, pp. 1598–1695, Apr. 2023, doi: 10.1002/alz.13016. [2] World Health Organization, “Dementia,” 2023. Accessed: Oct. 18, 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/dementia [3] K. A. N. N. P. Gunawardena, R. N. Rajapakse, and N. D. Kodikara, “Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural MRI data,” in 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland: IEEE, Nov. 2017, pp. 1–7. doi: 10.1109/M2VIP.2017.8211486. [4] S. Dwivedi, T. Goel, R. Sharma, and R. Murugan, “Structural MRI based Alzheimer’s Disease prognosis using 3D Convolutional Neural Network and Support Vector Machine,” in 2021 Advanced Communication Technologies and Signal Processing (ACTS), Rourkela, India: IEEE, Dec. 2021, pp. 1–4. doi: 10.1109/ACTS53447.2021.9708107. [5] R. S. Swarun, V. Navya, B. M C, and V. Thomas, “A Deep Learning Based Classification of Alzheimer’s Disease Using 3D CNN,” in 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST), Kochi, India: IEEE, Apr. 2024, pp. 1–5. doi: 10.1109/ICTEST60614.2024.10576122. [6] Y. Li, Y. Zhang, J. Wu, X. Zhang, L. Han, and X. Cui, “Multi-Attention-based Global 3D ResNet for Alzheimer’s Disease Diagnosis,” in 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan: IEEE, Jun. 2024, pp. 1–8. doi: 10.1109/IJCNN60899.2024.10650984. [7] X. Wang, J. Yi, and Y. Li, “Application of Fusion Model of 3D-ResNeXt and Bi-LSTM Network in Alzheimer’s Disease Classification,” in 2022 6th International Conference on Communication and Information Systems (ICCIS), Chongqing, China: IEEE, Oct. 2022, pp. 136–140. doi: 10.1109/ICCIS56375.2022.9998141. [8] Z. Xia et al., “A Novel End-to-End Hybrid Network for Alzheimer’s Disease Detection Using 3D CNN and 3D CLSTM,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA: IEEE, Apr. 2020, pp. 1–4. doi: 10.1109/ISBI45749.2020.9098621. [9] R. Kumari, S. Goel, and S. Das, “A Patch Based 3D CNN Approach for Diagnosing Early Stages of Alzheimer’s Disease by Applying OBL-WOA Algorithm,” in 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China: IEEE, Apr. 2023, pp. 455–459. doi: 10.1109/ICCCBDA56900.2023.10154706. [10] Y. Wu, Y. Zhou, W. Zeng, Q. Qian, and M. Song, “An Attention-Based 3D CNN With Multi-Scale Integration Block for Alzheimer’s Disease Classification,” IEEE J. Biomed. Health Inform., vol. 26, no. 11, pp. 5665–5673, Nov. 2022, doi: 10.1109/JBHI.2022.3197331. [11] R. Kumari, S. Goel, and S. Das, “A 3D Convolutional Neural Network Approach for Diagnosing Alzheimer’s Disease using Modified Owl Search Optimization Technique,” in TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), Hong Kong, Hong Kong: IEEE, Nov. 2022, pp. 1–7. doi: 10.1109/TENCON55691.2022.9977604. [12] N. Noshiri, M. A. Beck, C. P. Bidinosti, and C. J. Henry, “A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images,” Smart Agricultural Technology, vol. 5, p. 100316, Oct. 2023, doi: 10.1016/j.atech.2023.100316.
DOI: https://doi.org/10.22146/ijeis.103041
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