Deep Learning Implementation using Convolutional Neural Network for Alzheimer’s Classification

Adhigana  Priyatama; Zamah Sari; Yufis Azhar

doi:10.29207/resti.v7i2.4707

Adhigana Priyatama University of Muhammadiyah Malang
Zamah Sari University of Muhammadiyah Malang
Yufis Azhar University of Muhammadiyah Malang

DOI: https://doi.org/10.29207/resti.v7i2.4707

Keywords: Alzheimer's Disease, Image Classification, Deep Learning, Convolutional Neural Network, Transfer Learning

Abstract

Alzheimer's disease is the most common cause of dementia. Dementia refers to brain symptoms such as memory loss, difficulty thinking and problem solving and even speaking. This stage of development of neuropsychiatric symptoms is usually examined using magnetic resonance images (MRI) of the brain. The detection of Alzheimer's disease from data such as MRI using machine learning has been the subject of research in recent years. This technology has facilitated the work of medical experts and accelerated the medical process. In this study we target the classification of Alzheimer's disease images using convolutional neural network (CNN) and transfer learning (VGG16 and VGG19). The objective of this study is to classify Alzheimer's disease images into four classes that are recognized by medical experts and the results of this study are several evaluation metrics. Through experiments conducted on the dataset, this research has proven that the algorithm used is able to classify MRI of Alzheimer's disease into four classes known to medical experts. The accuracy of the first CNN model is 75.01%, the second VGG16 model is 80.10% and the third VGG19 model is 80.28%.

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