Predicting Alzheimer’s Disease Using Deep Learning Artificial Intelligence Together with a Pre-Trained VGG19 and Inception_v3 Models

Paul Teodorescu (), Silvia Ovreiu (), Madalina Zamfir () and Cristian Tirlea ()

Informatica Economica, 2024, vol. 28, issue 2, 17-34

Abstract: This paper presents two experiments in which, using artificial intelligence (specifically Deep Learning with convolutional neural networks), we were able to predict Alzheimer's disease based on MRI images. In order to have better results and to minimize the computational effort in the laboratory, two pre-trained AI models were used, models trained previ-ously on more than a million images from the ImageNet database (which provide tens of mil-lions of clean, labelled and sorted images). The top-layers of the models were trained, for our specific task of Alzheimer’s prediction, with 500 public MRI images from Kaggle, an online community of data scientists and machine learning engineers and a subsidiary of Google. In this paper we describe the code used in the laboratory for the specific task.

Keywords: MRI images; ImageNet; Feature extractor; Demented; Accuracy; Pre-trained; Target; Convolutional; Matrix (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:28:y:2024:i:2:p:17-34

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