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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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    Efficient image processing and machine learning approach for predicting retinal diseases

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    1530112, 15301025, 19341025_CSE.pdf (1023.Kb)
    Date
    2020-04
    Publisher
    Brac University
    Author
    Hasib, Mehadi Hasan
    Sultana, Tasnim
    Chowdhury, Chandrika
    Metadata
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    URI
    http://dspace.bracu.ac.bd/xmlui/handle/10361/14451
    Abstract
    As the computational technology and hadrware system improved over time, the use of neural network in image processing has become more and more prominent. Soon deep learning also caught the attention of the medical sector and started getting used in classify diseases. Lots of research are currently going on to predict retinal diseases using deep learning algorithms. However, very small amount of research have been conducted on predicting choroidal neovascularization (CNV), Diabetic Macular Edema (DME) and DRUSEN. In this paper, we have classified OCT images into 4 categories (CNV, DME, DRUSEN and natural retina) by using two deep learning algorithm (convolutional neural network and artificial neural network). Before passing the images into the neural network, we have performed a number of preprocessing methods on the images. Furthermore, we have implemented different model for each algorithms. Each model has varying numbers of hidden layer attached to it. After completing our research we have found out that, convolutional neural network with four hidden layers ou
    Keywords
    Image Processing; Deep Learning; Neural Network; Convolutional Neural Network; Artificial Neural Network; Retinal Disease
     
    Description
    This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 22-25).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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