Cargando…

Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture

Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often...

Descripción completa

Detalles Bibliográficos
Autores principales: Sajid, Muhammad Zaheer, Qureshi, Imran, Abbas, Qaisar, Albathan, Mubarak, Shaheed, Kashif, Youssef, Ayman, Ferdous, Sehrish, Hussain, Ayyaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137711/
https://www.ncbi.nlm.nih.gov/pubmed/37189539
http://dx.doi.org/10.3390/diagnostics13081439
_version_ 1785032532637515776
author Sajid, Muhammad Zaheer
Qureshi, Imran
Abbas, Qaisar
Albathan, Mubarak
Shaheed, Kashif
Youssef, Ayman
Ferdous, Sehrish
Hussain, Ayyaz
author_facet Sajid, Muhammad Zaheer
Qureshi, Imran
Abbas, Qaisar
Albathan, Mubarak
Shaheed, Kashif
Youssef, Ayman
Ferdous, Sehrish
Hussain, Ayyaz
author_sort Sajid, Muhammad Zaheer
collection PubMed
description Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect HR eye-related diseases using machine learning (ML) and deep learning (DL) techniques. Compared to ML methods, the CADx systems use DL techniques that require the setting of hyperparameters, domain expert knowledge, a huge training dataset, and a high learning rate. Those CADx systems have shown to be good for automating the extraction of complex features, but they cause problems with class imbalance and overfitting. By ignoring the issues of a small dataset of HR, a high level of computational complexity, and the lack of lightweight feature descriptors, state-of-the-art efforts depend on performance enhancement. In this study, a pretrained transfer learning (TL)-based MobileNet architecture is developed by integrating dense blocks to optimize the network for the diagnosis of HR eye-related disease. We developed a lightweight HR-related eye disease diagnosis system, known as Mobile-HR, by integrating a pretrained model and dense blocks. To increase the size of the training and test datasets, we applied a data augmentation technique. The outcomes of the experiments show that the suggested approach was outperformed in many cases. This Mobile-HR system achieved an accuracy of 99% and an F1 score of 0.99 on different datasets. The results were verified by an expert ophthalmologist. These results indicate that the Mobile-HR CADx model produces positive outcomes and outperforms state-of-the-art HR systems in terms of accuracy.
format Online
Article
Text
id pubmed-10137711
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-101377112023-04-28 Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture Sajid, Muhammad Zaheer Qureshi, Imran Abbas, Qaisar Albathan, Mubarak Shaheed, Kashif Youssef, Ayman Ferdous, Sehrish Hussain, Ayyaz Diagnostics (Basel) Article Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect HR eye-related diseases using machine learning (ML) and deep learning (DL) techniques. Compared to ML methods, the CADx systems use DL techniques that require the setting of hyperparameters, domain expert knowledge, a huge training dataset, and a high learning rate. Those CADx systems have shown to be good for automating the extraction of complex features, but they cause problems with class imbalance and overfitting. By ignoring the issues of a small dataset of HR, a high level of computational complexity, and the lack of lightweight feature descriptors, state-of-the-art efforts depend on performance enhancement. In this study, a pretrained transfer learning (TL)-based MobileNet architecture is developed by integrating dense blocks to optimize the network for the diagnosis of HR eye-related disease. We developed a lightweight HR-related eye disease diagnosis system, known as Mobile-HR, by integrating a pretrained model and dense blocks. To increase the size of the training and test datasets, we applied a data augmentation technique. The outcomes of the experiments show that the suggested approach was outperformed in many cases. This Mobile-HR system achieved an accuracy of 99% and an F1 score of 0.99 on different datasets. The results were verified by an expert ophthalmologist. These results indicate that the Mobile-HR CADx model produces positive outcomes and outperforms state-of-the-art HR systems in terms of accuracy. MDPI 2023-04-17 /pmc/articles/PMC10137711/ /pubmed/37189539 http://dx.doi.org/10.3390/diagnostics13081439 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sajid, Muhammad Zaheer
Qureshi, Imran
Abbas, Qaisar
Albathan, Mubarak
Shaheed, Kashif
Youssef, Ayman
Ferdous, Sehrish
Hussain, Ayyaz
Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture
title Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture
title_full Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture
title_fullStr Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture
title_full_unstemmed Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture
title_short Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture
title_sort mobile-hr: an ophthalmologic-based classification system for diagnosis of hypertensive retinopathy using optimized mobilenet architecture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137711/
https://www.ncbi.nlm.nih.gov/pubmed/37189539
http://dx.doi.org/10.3390/diagnostics13081439
work_keys_str_mv AT sajidmuhammadzaheer mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT qureshiimran mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT abbasqaisar mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT albathanmubarak mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT shaheedkashif mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT youssefayman mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT ferdoussehrish mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture
AT hussainayyaz mobilehranophthalmologicbasedclassificationsystemfordiagnosisofhypertensiveretinopathyusingoptimizedmobilenetarchitecture