IDEAS home Printed from https://ideas.repec.org/a/abf/journl/v56y2024i3p48124-48127.html
   My bibliography  Save this article

Convolutional Neural Networks for Enhancing Clinical Decision-Making

Author

Listed:
  • Kiran Sree Pokkuluri

    (Professor & Head, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, India)

  • SSSN Usha Devi N

    (Department of Computer Science and Engineering, University College of Engineering, India)

Abstract

Convolutional Neural Networks (CNNs) are a powerful technology that may be used to analyses medical imaging data and improve clinical decision-making. This abstract emphasizes their significance and ramifications for the medical field. CNNs are highly proficient in image segmentation, classification, and detection, which makes it possible to interpret medical images like MRIs, CT scans, and X-rays accurately and quickly. CNNs help physicians diagnose conditions, evaluate the effectiveness of treatments, and plan interventions more quickly and precisely by automatically recognizing and emphasizing important elements in images. Furthermore, CNNs enable the creation of computer-aided diagnostic systems that supplement human expertise by facilitating the integration of cutting-edge imaging technologies into clinical workflows. These technologies provide insightful decision assistance, assisting medical professionals in identifying small irregularities and formulating wise treatment suggestions.

Suggested Citation

  • Kiran Sree Pokkuluri & SSSN Usha Devi N, 2024. "Convolutional Neural Networks for Enhancing Clinical Decision-Making," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 56(3), pages 48124-48127, May.
    • Handle: RePEc:abf:journl:v:56:y:2024:i:3:p:48124-48127
    DOI: 10.26717/BJSTR.2024.56.008859
    as

    Download full text from publisher

    File URL: https://biomedres.us/pdfs/BJSTR.MS.ID.008859.pdf
    Download Restriction: no
    File URL: https://biomedres.us/fulltexts/BJSTR.MS.ID.008859.php
    Download Restriction: no
    File URL: https://libkey.io/10.26717/BJSTR.2024.56.008859?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:abf:journl:v:56:y:2024:i:3:p:48124-48127. 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: Angela Roy (email available below). General contact details of provider: .

    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.