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

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

alzheimer's disease; convolutional neural network; efficientnet-B0; efficientnet-B3

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-basedrecognition 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 ConvolutionalNeural 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 bothscenarios, the Exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the Exactness was 97.00%

Novia Adelia Ujilast1, Nuris Sabila Firdausita2, Christian Sri KusumaAditya3*, Yufis Azhar

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5457/888

Departement of Informatics, Faculty of Engineering, Muhammadiyah MalangUniversity, Malang, Indonesia

18-01-2024

FAJAR BAGUS W

PDF

ENGLISH

TEXT