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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Diabetic retinopathy detection using machine learning

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    14101013,17341009,13301087_CSE.pdf (520.3Kb)
    Date
    4/4/2018
    Publisher
    BRAC University
    Author
    Maliha, Maisha
    Tareque, Ahmed
    Roy, Sourav Saha
    Metadata
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    URI
    http://hdl.handle.net/10361/10149
    Abstract
    Diabetic Retinopathy (DR) is human eye disease among people with diabetics which causes damage to retina of eye and may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Effective treatments for DR are available though it requires early diagnosis and the continuous monitoring of diabetic patients. Also many physical tests like visual acuity test, pupil dilation, and optical coherence tomography can be used to detect diabetic retinopathy but are time consuming. The objective of our thesis is to give decision about the presence of diabetic retinopathy by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image. It will give us accuracy of which algorithm will be suitable and more accurate for prediction of the disease. Decision making for predicting the presence of diabetic retinopathy is performed using K-Nearest Neighbor, Random Forest, Support Vector Machine and Neural Networks.
    Keywords
    Diabetic retinopathy; Eye disease; Pupil dilation; Tomography; Machine learning
     
    Description
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (page 30-32).
     
    This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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