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...
Autores principales: | , , , , , , , |
---|---|
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 |