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Preliminary study of diabetic retinopathy classification from fundus images using deep learning model

Hoe, Yean Sam (2020) Preliminary study of diabetic retinopathy classification from fundus images using deep learning model. Final Year Project, UTAR.

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    Abstract

    Over the years, the cases of diabetes in Malaysia was increasing drastically. As a result, diabetic retinopathy had emerged among the diabetic patients. Diabetic retinopathy was a chronic eye disease that caused by diabetes, which would affect the eyesight and even blindness. Despite the fact that the disease was becoming more common, doctors were still conduct disease screening manually, which meant there was a risk of patients diagnosed incorrectly. The doctors were still using the traditional method on the diagnosis was because the lack of prediction data on diabetic retinopathy progression locally. Eventually, researches on the diagnosis were difficult to be conducted. Therefore, the preliminary study of the severity levels classification of diabetic retinopathy from fundus images using deep learning model was introduced in this project. Deep learning was a technique that could learn from the train fundus image dataset and conduct prediction on the similar test dataset automatically. The model architecture that used to train the dataset was DenseNet, which was a Convolutional Neural Network (CNN) based architecture. In the development of this project, various image pre-processing methods were done to enhance the image for training. Besides, data validation and image transforming techniques including data augmentation and test-time augmentation (TTA) were also used to evaluate training results and reduce overfitting respectively. The project involved the prediction testing on each image as well as the effects of data augmentation and TTA by observing the quadratic weighted kappa values. At the end of the project, a prediction model that able to predict and classify the severity labels of fundus images was built using deep learning model. The prediction model had achieved the quadratic weighted kappa score of 0.9308, whereas the overall accuracies attained were higher than 74% (estimated) without TTA on APTOS test dataset and 65% on Messidor-2 dataset, which were moderately accurate.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Hons)
    Depositing User: ML Main Library
    Date Deposited: 07 Jan 2021 20:39
    Last Modified: 07 Jan 2021 20:39
    URI: http://eprints.utar.edu.my/id/eprint/3952

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