IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i6p806-d1353991.html
   My bibliography  Save this article

Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification

Author

Listed:
  • Tamás Katona

    (Doctoral School of Informatics, University of Debrecen, 4032 Debrecen, Hungary
    Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary)

  • Gábor Tóth

    (Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary)

  • Mátyás Petró

    (Department of Radiology, Medical Imaging Insitute, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary)

  • Balázs Harangi

    (Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary)

Abstract

Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional neural networks (CNNs) such as VGG16, ResNet 50, and DenseNet 121, modifying their output layers and fine-tuning the models. We employ a novel optimization strategy using the Hyperband algorithm to efficiently search the hyperparameter space while adjusting the fully connected layers of the CNNs. The effectiveness of our approach is evaluated on the basis of the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metric. Our proposed methodology could assist in automated chest radiograph interpretation, offering a valuable tool that can be used by clinicians in the future.

Suggested Citation

  • Tamás Katona & Gábor Tóth & Mátyás Petró & Balázs Harangi, 2024. "Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification," Mathematics, MDPI, vol. 12(6), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:806-:d:1353991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/6/806/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/6/806/
    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:gam:jmathe:v:12:y:2024:i:6:p:806-:d:1353991. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.