MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture

  • Novia Adelia Ujilast Universitas Muhammadiyah Malang
  • Nuris Sabila Firdausita Universitas Muhammadiyah Malang
  • Christian Sri Kusuma Aditya Universitas Muhammadiyah Malang
  • Yufis Azhar Universitas Muhammadiyah Malang
Keywords: alzheimer's disease, convolutional neural network, efficientnet-B0, efficientnet-B3

Abstract

Alzheimer's disease is a neurodegenerative disorder or a condition characterized by the degeneration and damage of the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-based recognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes utilizing the Convolutional Neural Network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study utilized 6400 images that encompass four classes, namely Mild Demented, Moderate Demented, Non Demented, and Very Mild Demented. After conducting testing for both scenarios, the Exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the Exactness was 97.00%.

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Published
2024-01-18
How to Cite
Ujilast, N. A., Firdausita, N. S., Aditya, C. S. K., & Azhar, Y. (2024). MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 18 - 25. https://doi.org/10.29207/resti.v8i1.5457
Section
Information Technology Articles