• Login
    View Item 
    •   Mak IR Home
    • College of Computing and Information Sciences (CoCIS)
    • School of Computing and Informatics Technology (CIT)
    • School of Computing and Informatics Technology (CIT) Collection
    • View Item
    •   Mak IR Home
    • College of Computing and Information Sciences (CoCIS)
    • School of Computing and Informatics Technology (CIT)
    • School of Computing and Informatics Technology (CIT) Collection
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Automated diagnosis of malaria with scale invariant feature transforms and cascades of boosted classiers.

    Thumbnail
    View/Open
    ikae-cocis-masters-abstract.pdf (35.66Kb)
    ikae-cocis-masters-report.pdf (4.801Mb)
    Date
    2012-09
    Author
    Ikae, Catherin Omal
    Metadata
    Show full item record
    Abstract
    In recent years, many research have been devoted to the exploration of techniques of malaria diagnosis; however, few have devoted time to automated diagnosis using computer vision techniques. In fact, no attempts have been made in the use of Scale Invariant Feature Transforms (SIFT) and Haar cascades for malaria diagnosis. Likewise object detection in images has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in microscopic images of a blood smear, a robust and efficient detection is still considered as an open and challenging problem. This dissertation therefore presents approaches of developing malaria diagnosis with the SIFT and Haar cascade features. Thick blood smear was used and parasite regions within the image were extracted and trained. The performance evaluations of the SIFT diagnostic methodology being at sensitivity of 98.78% and the cascade methodology being at sensitivity of 99.70% against other computer vision techniques demonstrate the promise and superiority of these approaches in terms of malaria diagnosis.
    URI
    http://hdl.handle.net/10570/2718
    Collections
    • School of Computing and Informatics Technology (CIT) Collection

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of Mak IRCommunities & CollectionsTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy TypeThis CollectionTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV