IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v28y2024i2p17-34.html
   My bibliography  Save this article

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

Author

Listed:
  • Paul TEODORESCU
  • Silvia OVREIU
  • Madalina ZAMFIR
  • Cristian TIRLEA

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.

Suggested Citation

  • Paul TEODORESCU & Silvia OVREIU & Madalina ZAMFIR & Cristian TIRLEA, 2024. "Predicting Alzheimer’s Disease Using Deep Learning Artificial Intelligence Together with a Pre-Trained VGG19 and Inception_v3 Models," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 28(2), pages 17-34.
    • Handle: RePEc:aes:infoec:v:28:y:2024:i:2:p:17-34
    as

    Download full text from publisher

    File URL: https://revistaie.ase.ro/content/110/02%20-%20teodorescu,%20ovreiu,%20zamfir,%20tirlea.pdf
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:28:y:2024:i:2:p:17-34. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.