Deteksi Penyakit Alzheimer Melalui MRI Menggunakan Metode Pendekatan Gabungan Convolutional Neural Network dan Ensemble Learning. (Detection of Alzheimer’s Disease Through MRI using a Combined Approach of Convolutional Neural Network and Ensemble Learning).

Latupeirissa, Jedija Lovita (2024) Deteksi Penyakit Alzheimer Melalui MRI Menggunakan Metode Pendekatan Gabungan Convolutional Neural Network dan Ensemble Learning. (Detection of Alzheimer’s Disease Through MRI using a Combined Approach of Convolutional Neural Network and Ensemble Learning). Undergraduate thesis, Universitas 17 Agustus 1945.

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Abstract

Alzheimer's disease is a common illness experienced by older adults, especially the elderly. This disease is characterized by brain damage that leads to a decline in a person's ability to speak, remember, understand, and think. The deep learning technique widely applied to detect structural changes in the brain using Magnetic Resonance Imaging (MRI) is Convolutional Neural Network (CNN) due to its efficiency in automatic feature learning using multilayer perceptrons. Additionally, Ensemble Learning (EL) has proven its system robustness by using multiple models. The dataset used comes from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and normal controls (CN). The CNN model used consists of 12 layers, and the EL method applied is bootstrap aggregating (bagging) with 5 model sampling. The testing results of the CNN and EL models on 40% of the test dataset without preprocessing achieved an accuracy of 78.94%. The preprocessing process includes image normalization, skull stripping, smoothing, and gray normalization, resulting in the ensemble model with preprocessing achieving an accuracy of 91.5789% from 40% of 474 datasets in image classification. The model performance evaluation includes overall accuracy, prediction results, and comparison with actual labels, showing that the preprocessing and ensemble methods provide accurate and reliable classification performance.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Alzheimer, CNN, Ensemble, 3D
Subjects:	Q Science > QM Human anatomy T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	1462000154 Jedija Jedija
Date Deposited:	05 Sep 2001 15:35
Last Modified:	06 Oct 2001 20:37
URI:	http://repository.untag-sby.ac.id/id/eprint/35022

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