Prediction of Arteriovenous Nicking for Hypertensive Retinopathy Using Deep Learning

Kriplani, Himanshu; Patel, M.; Roy, Sudipta

doi:10.1007/978-981-13-8676-3_13

Cited by 5 publications

(6 citation statements)

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“…In addition, we have performed state-of-the-art comparisons with four recent HR systems. DL-based HR models are utilized, such as DenseHyper [ 31 ], Arsalan-HR [ 38 ], Kriplani-AVR [ 39 ] and Tag-Semantic [ 40 ] because of the ease of their implementation. Mostly, they were developed to detect lesions from retinograph images for diabetic retinopathy (DR) or HR stage classification.…”

Section: Resultsmentioning

confidence: 99%

“…In comparison to other state-of-the-art approaches for HR recognition that use deep learning (DL) models, few research efforts use DL approaches for classifying HR from fundus videos. These four DL-based HR models are utilized: DenseHyper [ 31 ], Arsalan-HR [ 38 ], Kriplani-AVR [ 39 ] and Tag-Semantic [ 40 ]. This is because of their ease of implementation.…”

Section: Resultsmentioning

confidence: 99%

“…The identification accuracy of 98.6 percent is reported in Arsalan-HR [ 38 ] due to the use of small training and the testing dataset of HR. The authors in the Kriplani-AVR [ 39 ] scheme used the aggregate residual neural network (ResNeXt) model as a classifier, and they achieved 94% of accuracy on the structured analysis of the retina (STARE) dataset. For segmentation and feature extraction, the device employs image processing techniques.…”

Section: Resultsmentioning

confidence: 99%

“…The classifier’s input is a function vector made up of the A/V Ratio rather than the fundus picture. To equate the proposed HYPER-RETINO method to Kriplani-AVR [ 39 ], we follow the same steps and tested it on the model Kriplani-AVR. On average, the Kriplani-AVR model achieves SE of 74.5%, SP of 73.5%, ACC of 74% and AUC of 0.74, which is slightly higher than the Arsalan-HR [ 38 ] system.…”

Section: Resultsmentioning

confidence: 99%

“…However, many HR-related lesions are important to detect for the recognition of the stage of HR. Similarly, in [ 39 ], the authors developed a Kriplani-AVR system to recognize two stages of HR based on the AVR ratio of blood vessels. The Kriplani-AVR system was tested on the DRIVE dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Abbas

Qureshi

Ibrahim

2021

Sensors

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The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings.

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