Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading

The increasing number of cases of undiagnosed diabetic patients worldwide has been worrisome as more underlying diseases or side effects of diabetes can go undetected, such as Diabetic Retinopathy (DR). The lack of medical assistance in remote areas is a huge problem as it is a long travel distance...

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Main Author:	Nurul Mirza Afiqah, Tajudin
Format:	Thesis
Language:	English English
Published:	2023
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/43167/5/retricted%20tesis_Nurul%20Mirza.pdf http://ir.unimas.my/id/eprint/43167/10/Nurul%20Mirza%20Afiqah%20ft.pdf
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spelling	my-unimas-ir.431672024-08-13T02:34:26Z Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading 2023-10-20 Nurul Mirza Afiqah, Tajudin QA75 Electronic computers. Computer science The increasing number of cases of undiagnosed diabetic patients worldwide has been worrisome as more underlying diseases or side effects of diabetes can go undetected, such as Diabetic Retinopathy (DR). The lack of medical assistance in remote areas is a huge problem as it is a long travel distance to the city and comes with a high cost. Hence, studies have been working towards automating medical diagnosis by incorporating Artificial Intelligence (AI) in their system. The rapid growth of technologies and AI has led to the development of Deep Learning (DL), in which its algorithms are stacked in a hierarchy of increasing complexity and abstraction. Thus, by employing DL in the study, automated DR screening will ease the process of diagnosing DR and grading the severity level of the disease. The EyePACS data used in the study are from a competition organized in Kaggle, which consists of 35,126 training and 1,794 testing images that have been graded to their severity level; 0: No DR; 1: mild Non-Proliferative DR(NPDR); 2: Moderate NPDR; 3: Severe NPDR; and 4: Proliferative DR (PDR). The datasets are fed to the Convolutional Neural Network (CNN) in which the model was trained to learn to classify the features of each severity level of DR. Several CNNs are compared in terms of their performance to grade DR, which is AlexNet, ResNet-18, GoogLeNet, Inception-V3, MobileNetV2, and VGG-16. Then, Inception-V3 is picked to be further investigated under different configurations and parameter settings. The features that the model learnt for each class are explored to understand the process behind CNN layers. The final testing accuracy achieved by the model is 80.10%, with sensitivity of 0.4248 and specificity of 0.8860. UNIMAS 2023-10 Thesis http://ir.unimas.my/id/eprint/43167/ http://ir.unimas.my/id/eprint/43167/5/retricted%20tesis_Nurul%20Mirza.pdf text en staffonly http://ir.unimas.my/id/eprint/43167/10/Nurul%20Mirza%20Afiqah%20ft.pdf text en validuser masters Universiti Malaysia Sarawak Department of Electrical and Electronic Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Nurul Mirza Afiqah, Tajudin Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading
description	The increasing number of cases of undiagnosed diabetic patients worldwide has been worrisome as more underlying diseases or side effects of diabetes can go undetected, such as Diabetic Retinopathy (DR). The lack of medical assistance in remote areas is a huge problem as it is a long travel distance to the city and comes with a high cost. Hence, studies have been working towards automating medical diagnosis by incorporating Artificial Intelligence (AI) in their system. The rapid growth of technologies and AI has led to the development of Deep Learning (DL), in which its algorithms are stacked in a hierarchy of increasing complexity and abstraction. Thus, by employing DL in the study, automated DR screening will ease the process of diagnosing DR and grading the severity level of the disease. The EyePACS data used in the study are from a competition organized in Kaggle, which consists of 35,126 training and 1,794 testing images that have been graded to their severity level; 0: No DR; 1: mild Non-Proliferative DR(NPDR); 2: Moderate NPDR; 3: Severe NPDR; and 4: Proliferative DR (PDR). The datasets are fed to the Convolutional Neural Network (CNN) in which the model was trained to learn to classify the features of each severity level of DR. Several CNNs are compared in terms of their performance to grade DR, which is AlexNet, ResNet-18, GoogLeNet, Inception-V3, MobileNetV2, and VGG-16. Then, Inception-V3 is picked to be further investigated under different configurations and parameter settings. The features that the model learnt for each class are explored to understand the process behind CNN layers. The final testing accuracy achieved by the model is 80.10%, with sensitivity of 0.4248 and specificity of 0.8860.
format	Thesis
qualification_level	Master's degree
author	Nurul Mirza Afiqah, Tajudin
author_facet	Nurul Mirza Afiqah, Tajudin
author_sort	Nurul Mirza Afiqah, Tajudin
title	Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading
title_short	Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading
title_full	Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading
title_fullStr	Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading
title_full_unstemmed	Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading
title_sort	development of deep learning classification model for diabetic retinopathy detection and grading
granting_institution	Universiti Malaysia Sarawak
granting_department	Department of Electrical and Electronic Engineering
publishDate	2023
url	http://ir.unimas.my/id/eprint/43167/5/retricted%20tesis_Nurul%20Mirza.pdf http://ir.unimas.my/id/eprint/43167/10/Nurul%20Mirza%20Afiqah%20ft.pdf
_version_	1811771567508553728

Development of Deep Learning Classification Model for Diabetic Retinopathy Detection and Grading

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