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A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES

The eyes fulfill a vital function in daily life as the primary sensory organs for perceiving the world. Neglecting proper attention and care for eye health can lead to vision loss or impairment, with severe eye diseases having the potential to cause complete blindness. The main problem addressed...

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Bibliographic Details
Main Author: OON, WENG WAI
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://ir.unimas.my/id/eprint/44192/1/OON%20WENG%20WAI%20%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/44192/2/OON%20WENG%20WAI%20%20ft.pdf
http://ir.unimas.my/id/eprint/44192/
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spelling my.unimas.ir.441922024-01-17T08:55:38Z http://ir.unimas.my/id/eprint/44192/ A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES OON, WENG WAI QA75 Electronic computers. Computer science The eyes fulfill a vital function in daily life as the primary sensory organs for perceiving the world. Neglecting proper attention and care for eye health can lead to vision loss or impairment, with severe eye diseases having the potential to cause complete blindness. The main problem addressed in this study is the global burden of eye diseases, worsened by a shortage of ophthalmologists and insufficient training in using essential instruments. These factors contribute to delays in diagnosis and treatment, underscoring the necessity for an automated approach to classify eye diseases from retinal images. Therefore, this study aims to compare various types of deep learning models, such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) to classify specific eye diseases, including cataracts, diabetic retinopathy, glaucoma, and normal classes. Based on a review of related research, it is evident that CNN is well�suited for image classification. Therefore, this study will train models using a proposed CNN, VGG19, and InceptionV3 architectures. In this study, the dataset collected from Kaggle underwent preprocessing involving histogram equalization and image segmentation techniques. It was then divided into training (80%), validation (10%), and testing (10%) sets. Performance evaluation of the models will be based on accuracy, precision, and recall. Techniques such as early stopping, model checkpointing, hyperparameter tuning, and fine-tuning were employed to optimize the models and improve their accuracy. VGG19 Model 3 with 16 convolutional layers, 3 dense layers (each with 300 neurons), a dropout rate of 0.5, and Adam optimizer with a learning rate of 0.0001, achieved impressive training accuracy of 90% and testing accuracy of 87%. The utilization of deep learning models in clinical settings could help improve the accuracy and speed of eye disease classification, leading to timely interventions and improved patient outcomes. Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44192/1/OON%20WENG%20WAI%20%20%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/44192/2/OON%20WENG%20WAI%20%20ft.pdf OON, WENG WAI (2023) A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
OON, WENG WAI
A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES
description The eyes fulfill a vital function in daily life as the primary sensory organs for perceiving the world. Neglecting proper attention and care for eye health can lead to vision loss or impairment, with severe eye diseases having the potential to cause complete blindness. The main problem addressed in this study is the global burden of eye diseases, worsened by a shortage of ophthalmologists and insufficient training in using essential instruments. These factors contribute to delays in diagnosis and treatment, underscoring the necessity for an automated approach to classify eye diseases from retinal images. Therefore, this study aims to compare various types of deep learning models, such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) to classify specific eye diseases, including cataracts, diabetic retinopathy, glaucoma, and normal classes. Based on a review of related research, it is evident that CNN is well�suited for image classification. Therefore, this study will train models using a proposed CNN, VGG19, and InceptionV3 architectures. In this study, the dataset collected from Kaggle underwent preprocessing involving histogram equalization and image segmentation techniques. It was then divided into training (80%), validation (10%), and testing (10%) sets. Performance evaluation of the models will be based on accuracy, precision, and recall. Techniques such as early stopping, model checkpointing, hyperparameter tuning, and fine-tuning were employed to optimize the models and improve their accuracy. VGG19 Model 3 with 16 convolutional layers, 3 dense layers (each with 300 neurons), a dropout rate of 0.5, and Adam optimizer with a learning rate of 0.0001, achieved impressive training accuracy of 90% and testing accuracy of 87%. The utilization of deep learning models in clinical settings could help improve the accuracy and speed of eye disease classification, leading to timely interventions and improved patient outcomes.
format Final Year Project Report
author OON, WENG WAI
author_facet OON, WENG WAI
author_sort OON, WENG WAI
title A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES
title_short A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES
title_full A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES
title_fullStr A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES
title_full_unstemmed A DEEP LEARNING APPROACH FOR MULTICLASS CLASSIFICATION OF RETINAL IMAGES
title_sort deep learning approach for multiclass classification of retinal images
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44192/1/OON%20WENG%20WAI%20%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/44192/2/OON%20WENG%20WAI%20%20ft.pdf
http://ir.unimas.my/id/eprint/44192/
_version_ 1789430372947197952
score 13.211869

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