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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Early detection of parkinson’s disease using image processing and artificial neural network

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    14101080,14101121,14101128_CSE.pdf (1.471Mb)
    Date
    2018-04
    Publisher
    BRAC University
    Author
    Rumman, Mosarrat
    Tasneem, Abu Nayeem
    Farzana, Sadia
    Metadata
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    URI
    http://hdl.handle.net/10361/10151
    Abstract
    Early detection of Parkinson‟s Disease (PD) is very crucial for effective management and treatment of the disease. Dopaminergic images such as Single Photon Emission Tomography (SPECT) using 123I-Ioflupane can substantially detect Parkinson‟s Disease at an early stage. However, till today, these images are mostly interpreted by humans which can manifest interobserver variability and inconsistency. To improve the imaging diagnosis of PD, we propose a model in this paper, for early detection of Parkinson‟s disease using Image Processing and Artificial Neural Network (ANN). The model used 200 SPECT images, 100 of healthy normal and 100 of PD, obtained from Parkinson‟s Progression Marker‟s Initiative (PPMI) database and processed them to find the area of Caudate and Putamen which is the Region of Interest (ROI) for this study. The area values were then fed to the ANN which is hypothesized to mimic the pattern recognition of a human observer. The simple but fast ANN built, could classify subjects with and without PD with an accuracy of 94%, sensitivity of 100% and specificity of 88%. Hence it can be inferred that the proposed system has the potential to be an effective way to aid the clinicians in the accurate diagnosis of Parkinson‟s disease.
    Keywords
    Image processing; Artificial neural network; Parkinson’s disease; Early detection
     
    Description
    This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 43-45).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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