An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture

Abbas, Qaisar; Qureshi, Imran; Ibrahim, Mostafa E. A.

doi:10.3390/s21206936

Cited by 24 publications

(13 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the framework of DenseNet, Abbas et al [23] developed a HYPER-RETINO system by segmenting HRrelated lesions to assist in the clinical staging diagnosis of HR. After ten times cross-verification, the sensitivity of the system was 90.5%, the specificity was 91.5%, the accuracy was 92.6%, the F1 score was 92%, and the AUC was 0.915.…”

Section: Application Of Ai Image Analysis In Hypertensivementioning

confidence: 99%

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

Chen

Liu

et al. 2022

Disease Markers

View full text Add to dashboard Cite

The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet’s disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.

show abstract

Section: Application Of Ai Image Analysis In Hypertensivementioning

confidence: 99%

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

Chen

Liu

et al. 2022

Disease Markers

View full text Add to dashboard Cite

show abstract

“…The name of this condition is hypertensive retinopathy (HR). A hypertensive retinopathy (HYPER-RETINO) framework has been developed by authors in [14] to classify HR based on five grades. Based on previously trained HR-related lesions, a HYPER-RETINO system was implemented.…”

Section: Hypertensive Retinopathymentioning

confidence: 99%

“…To review the classification of hypertensive retinopathy, the authors in [20] have proposed a method to calculate the multiple uses of artery and vein diameter ratio (AVR) in addition to changes in position with the optic disc in retinal images utilizing Deep Neural Networks (DNN) and Boltzmann Machines approach.…”

Section: Hypertensive Retinopathymentioning

confidence: 99%

A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy

M¹,

Prasad²,

Kulkarni³

et al. 2022

IJAIA

View full text Add to dashboard Cite

Deep learning models are applied seamlessly across various computer vision tasks like object detection, object tracking, scene understanding and further. The application of cutting-edge deep learning (DL) models like U-Net in the classification and segmentation of medical images on different modalities has established significant results in the past few years. Ocular diseases like Diabetic Retinopathy (DR), Glaucoma, Age-Related Macular Degeneration (AMD / ARMD), Hypertensive Retina (HR), Cataract, and dry eyes can be detected at the early stages of disease onset by capturing the fundus image or the anterior image of the subject’s eye. Early detection is key to seeking early treatment and thereby preventing the disease progression, which in some cases may lead to blindness. There is a plethora of deep learning models available which have established significant results in medical image processing and specifically in ocular disease detection. A given task can be solved by using a variety of models and or a combination of them. Deep learning models can be computationally expensive and deploying them on an edge device may be a challenge. This paper provides a comprehensive report and critical evaluation of the various deep learning architectures that can be used to segment and classify ocular diseases namely Glaucoma and Hypertensive Retina on the posterior images of the eye. This review also compares the models based on complexity and edge deployability.

show abstract

“…Moreover, the classifier and generator work together to discriminate the domain images [14]. A res-guided UNet [15] is developed as a generator and res-guided sampling blocks are replaced by traditional convolution. Supervised learning usually requires a large training dataset to incorporate all possible variations.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Adversarial Network for Detection and Classification of Myopic燤aculopathy

Abbas¹,

Baig²,

Hussain³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is to design and train the DLs to automatically identify and classify PM into different classes. In this article, we have developed a new class of DL models (SAN-FSL) for the segmentation and detection of PM through semantic adversarial networks (SAN) and few-short learning (FSL) methods, respectively. Compared to DL methods, the conventional segmentation methods use supervised learning models, so they (a) require a lot of data for training and (b) fixed weights are used after the completion of the training process. To solve such problems, the FSL technique was employed for model training with few samples. The ability of FSL learning in UNet architectures is being explored, and to fine-tune the weights, a few new samples are being provided to the UNet. The outcomes show improvement in the detection area and classification of PM stages. Betterment in the result is observed by sensitivity (SE) of 95%, specificity (SP) of 96%, and area under the receiver operating curve (AUC) of 98%, and the higher F1-score is achieved using 10-fold crossvalidation. Furthermore, the obtained results confirmed the superiority of the SAN-FSL method.

show abstract

An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture

Cited by 24 publications

References 49 publications

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy

A Semantic Adversarial Network for Detection and Classification of Myopic燤aculopathy

Contact Info

Product

Resources

About