IDEAS home Printed from https://ideas.repec.org/a/igg/jcac00/v12y2022i1p1-13.html
   My bibliography  Save this article

Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning

Author

Listed:
  • Anil B. C.

    (JSS Academy of Technical Education, Bengaluru, India & Visvesvaraya Technological University, Belagavi, India)

  • Dayananda P.

    (JSS Academy of Technical Education, Bengaluru, India & Visvesvaraya Technological University, Belagavi, India)

  • Nethravathi B.

    (JSS Academy of Technical Education, Bengaluru, India & Visvesvaraya Technological University, Belagavi, India)

  • Mahesh S. Raisinghani

    (Texas Woman's University, USA)

Abstract

Liver cancer is one the most common forms of cancer. As per statistics in 2018 published by World Health Organization, a quarter of all cancer cases are caused by infections, particularly prevalent in developing countries, including hepatitis B, which is linked to liver cancer. The mortality rate is higher in liver cancer as compared to other types of cancer. Quick and reliable diagnosis tools are of paramount importance for detecting and treating liver cancer in early stage, thus improving the likely course of a medical condition of patient. We have developed a cloud-based solution for liver tumour Segmentation, Classification and Detection in CT images based on GoogleNet architecture of Convolutional Neural Network. Experiment is carried out with training and test sets derived from TCIA repository. The results yield 96.7% accuracy for classification of tumour cells. GoogleNet architecture is used for implementation. The GoogleNet has 70,000 images in diagnosis of malignant tumor in liver cancer, providing a rich database for testing. Our algorithm has been deployed in Azure cloud.

Suggested Citation

  • Anil B. C. & Dayananda P. & Nethravathi B. & Mahesh S. Raisinghani, 2022. "Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(1), pages 1-13, January.
    • Handle: RePEc:igg:jcac00:v:12:y:2022:i:1:p:1-13
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.2022010109
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jcac00:v:12:y:2022:i:1:p:1-13. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.