Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah

The screening of diabetic retinopathy (DR) affects the visual inspection of retina images taken by ophthalmologists to detect the specific signs of pathology such as exudate, haemorrhage (HEM) and microaneurysm (MA). Currently, this process is conducted manually in many hospitals. Therefore, it i...

Full description

Saved in:
Bibliographic Details
Main Author: Abu Samah, Abdul Hafiz
Format: Thesis
Language:English
Published: 2021
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/60075/1/60075.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uitm-ir.60075
record_format uketd_dc
spelling my-uitm-ir.600752022-05-23T00:45:11Z Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah 2021-08 Abu Samah, Abdul Hafiz Algorithms Medical technology The screening of diabetic retinopathy (DR) affects the visual inspection of retina images taken by ophthalmologists to detect the specific signs of pathology such as exudate, haemorrhage (HEM) and microaneurysm (MA). Currently, this process is conducted manually in many hospitals. Therefore, it is time-costing and risky for humans to make mistake. An automatic and accurate system for detection DR signs can significantly help ophthalmologists to make best decision for early treatment thus reducing risk of vision loss. In general, this thesis introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. To improve the performance from current systems, this work has investigation on different of image pre-processing enhancement technique to support accuracy on deep learning for DR classification. For the image enhancement process, this thesis has proposed high-pass filter combined with histogram equalization and de-haze algorithm respectively to improve the visual quality of fundus images. Based on investigation different architecture and parameter, the suitable deep learning model has been presented to get optimize best result and testing time. To solving pattern classification problem, the optimization deep learning architecture and parameter by using four convolution layers is set up to classify the three pathological signs; HEM, MA and exudate. Two public online datasets of fundus image, e-Ophtha and DIARETDB1 are used to evaluate the performance of this system. The performance of the system demonstrates the effectiveness of image enhancement in term of test accuracy percentage. From training and testing results using enhanced DR images, the both pre-processing steps has proven slight improvement of deep learning in the overall accuracy by 4.3% and 3.6% supported by enhancement 1 and 2 respectively, compared to those original images with no enhancement for both datasets. 2021-08 Thesis https://ir.uitm.edu.my/id/eprint/60075/ https://ir.uitm.edu.my/id/eprint/60075/1/60075.pdf text en public masters Universiti Teknologi MARA College of Engineering
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
language English
topic Algorithms
Medical technology
spellingShingle Algorithms
Medical technology
Abu Samah, Abdul Hafiz
Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
description The screening of diabetic retinopathy (DR) affects the visual inspection of retina images taken by ophthalmologists to detect the specific signs of pathology such as exudate, haemorrhage (HEM) and microaneurysm (MA). Currently, this process is conducted manually in many hospitals. Therefore, it is time-costing and risky for humans to make mistake. An automatic and accurate system for detection DR signs can significantly help ophthalmologists to make best decision for early treatment thus reducing risk of vision loss. In general, this thesis introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. To improve the performance from current systems, this work has investigation on different of image pre-processing enhancement technique to support accuracy on deep learning for DR classification. For the image enhancement process, this thesis has proposed high-pass filter combined with histogram equalization and de-haze algorithm respectively to improve the visual quality of fundus images. Based on investigation different architecture and parameter, the suitable deep learning model has been presented to get optimize best result and testing time. To solving pattern classification problem, the optimization deep learning architecture and parameter by using four convolution layers is set up to classify the three pathological signs; HEM, MA and exudate. Two public online datasets of fundus image, e-Ophtha and DIARETDB1 are used to evaluate the performance of this system. The performance of the system demonstrates the effectiveness of image enhancement in term of test accuracy percentage. From training and testing results using enhanced DR images, the both pre-processing steps has proven slight improvement of deep learning in the overall accuracy by 4.3% and 3.6% supported by enhancement 1 and 2 respectively, compared to those original images with no enhancement for both datasets.
format Thesis
qualification_level Master's degree
author Abu Samah, Abdul Hafiz
author_facet Abu Samah, Abdul Hafiz
author_sort Abu Samah, Abdul Hafiz
title Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
title_short Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
title_full Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
title_fullStr Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
title_full_unstemmed Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
title_sort classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / abdul hafiz abu samah
granting_institution Universiti Teknologi MARA
granting_department College of Engineering
publishDate 2021
url https://ir.uitm.edu.my/id/eprint/60075/1/60075.pdf
_version_ 1783735080519204864