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All Journal CogITo Smart Journal
Djamal, Eleonora
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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MRI Image Analysis for Alzheimer’s Disease Detection Using Transfer Learning: VGGNet vs. EfficientNet Sandag, Green Arther; Djamal, Eleonora; Tangka, George Morris William; Taju, Semmy Wellem
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.836.580-592

Abstract

This study focuses on developing an effective Alzheimer's disease (AD) classification model using MRI images and transfer learning. This research targets individuals aged 65 and above who are affected by the predominant form of dementia and utilizes an Alzheimer's Disease MRI Image dataset from Kaggle. Model selection involved options like EfficientNetB1, B3, B5, B7, VGG16, and VGG19. Two scenarios with distinct batch sizes (10 and 20) were explored in the model creation process. Evaluation, using a confusion matrix, determined that the EfficientNetB5 model yielded the highest accuracy at 99.22%, surpassing other models such as EfficientNetB1, B3, B7, VGG16, and VGG19. Notably, this research highlights the superior performance of EfficientNet over VGGNet in transfer learning for analyzing Alzheimer's disease MRI images. The study concludes with the implementation of a simple web system for testing model outcomes. Overall, the investigation underscores the efficacy of Convolutional Neural Network (CNN) modeling in Alzheimer's disease analysis and identifies EfficientNetB5 as the optimal model for accurate classification.
Co-Authors George Morris William Tangka Green Arther Sandag Semmy Wellem Taju