• Login
    View Item 
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Book Chapters
    • View Item
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Book Chapters
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Effat University RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentThis CollectionPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentProfilesView

    My Account

    Login

    Statistics

    Display statistics

    Advanced pattern recognition tools for disease diagnosis

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Author
    Subasi, Abdulhamit cc
    Panigrahi, Abhranta
    Patil, Bhalchandra Sunil
    Canbaz, Abdullah
    Klén, Riku
    Subject
    COVID-19 detection
    Chest X-ray
    Convolutional Neural Networks
    Deep Learning
    Transfer Learning
    Date
    2022
    
    Metadata
    Show full item record
    Abstract
    Machine learning (ML) uses statistical theory to create models from data samples. Using the predictive and statistical models, computers can clean and curate the data, interpret and predict the outcomes of certainties (or uncertainties) with precise accuracy. Of course, the interpretation of the produced results and algorithmic solution designed for each problem needs to be fine-tuned and proficient for the target problem. Biomedical images relevant to different diseases are recorded from a body and are generally employed to diagnose precise physiological or pathological conditions. The objective of biomedical image analysis is exact modeling by using pattern recognition and computer vision to diagnose diseases by employing ML techniques. This chapter explains how artificial intelligence (AI) and ML techniques are utilized in disease diagnosis. An automated COVID-19 diagnosis approach based on deep feature extraction is also presented. After extracting features using deep transfer learning (DTL), the X-ray images are fed into the shallow ML model to diagnose COVID-19 from X-ray images. With chest X-ray, a patient can be identified as a potential COVID-19 patient and can be quarantined. X-ray equipment are already accessible in most hospitals, and already digitized. Since X-ray images are high dimensional data, a Convolutional Neural Network based feature extraction via transfer learning models are appropriate for the diagnosis of COVID-19. It may help an inpatient environment where the existing programs find it difficult to determine whether to keep the patient inward with other patients or separate them. This technique will also help classify patients with high COVID-19 risk who need to repeat testing with a false negative RT-PCR
    Department
    Computer Science
    Publisher
    Academic Press
    Book title
    5G IoT and Edge Computing for Smart Healthcare
    DOI
    https://doi.org/10.1016/B978-0-323-90548-0.00011-5
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/B978-0-323-90548-0.00011-5
    Scopus Count
    Collections
    Book Chapters

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.