Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures

Arsalan, Muhammad; Haider, Adnan; Choi, Jiho; Park, Kang Ryoung

doi:10.3390/jpm12010007

Cited by 22 publications

(10 citation statements)

References 57 publications

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“…After testing, the AUC, accuracy, sensitivity, and specificity of the HR diagnosis model were 0.895, 0.8237, 0.8129, and 0.8275, respectively. To assist clinicians in screening HR, Arsalan et al (2021) 2022) collected 120,002 fundus photos and used a convolutional neural network to create a retinal AI diagnosis system (RAIDS) for the diagnosis of 10 types of retinal diseases, including HR. They randomly divided 120,002 fundus photos into training, test, and validation datasets and used them in the training and validation of the system.…”

Section: Application Of Artificial Intelligence In Hypertensive Retin...mentioning

confidence: 99%

“…After testing, the AUC, accuracy, sensitivity, and specificity of the HR diagnosis model were 0.895, 0.8237, 0.8129, and 0.8275, respectively. To assist clinicians in screening HR, Arsalan et al (2021) constructed an AI screening model using a dual-stream fusion network (DSF-Net) and a dual-stream aggregation network (DSA-Net). They evaluated the performance of the model using the DRIVE, STARE, and CHASE-DB1 dataset.…”

Section: Application Of Artificial Intelligence In Retinal Vascular D...mentioning

confidence: 99%

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Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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Section: Application Of Artificial Intelligence In Hypertensive Retin...mentioning

confidence: 99%

“…After testing, the AUC, accuracy, sensitivity, and specificity of the HR diagnosis model were 0.895, 0.8237, 0.8129, and 0.8275, respectively. To assist clinicians in screening HR, Arsalan et al (2021) constructed an AI screening model using a dual-stream fusion network (DSF-Net) and a dual-stream aggregation network (DSA-Net). They evaluated the performance of the model using the DRIVE, STARE, and CHASE-DB1 dataset.…”

Section: Application Of Artificial Intelligence In Retinal Vascular D...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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“…Many research articles [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] have focused on how to make small, effective networks that can be used for many different things. Researchers have tried out a wide range of methods, such as training network models and making older models smaller.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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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.

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“…Tan et al [31] introduced skeletal prior and contrast loss and proposed a new network named SkelCon, which is able to improve the integrity and continuity of thin blood vessels. Arsalan et al [32] designed a dual-stream fusion network (DSF-Net) and a dual-stream aggregation network (DSA-Net) for the task of semantic segmentation of retinal fundus images. Following this, Arsalan et al proposed a pooling-free residual segmentation network PLRS-Net [33] with stepped convolution to provide a pooling effect for better retinal vessel segmentation sensitivity.…”

Section: Related Work Of Cnn On Image Segmentationmentioning

confidence: 99%

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN

Jiang

Liang

Cheng

et al. 2022

Sensors

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Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited. Transformer, on the other hand, performs well in modeling long-distance dependencies. Therefore, in this paper, a new network model MTPA_Unet is designed from the perspective of extracting connections between local detailed features and making complements using long-distance dependency information, which is applied to the retinal vessel segmentation task. MTPA_Unet uses multi-resolution image input to enable the network to extract information at different levels. The proposed TPA module not only captures long-distance dependencies, but also focuses on the location information of the vessel pixels to facilitate capillary segmentation. The Transformer is combined with the convolutional neural network in a serial approach, and the original MSA module is replaced by the TPA module to achieve finer segmentation. Finally, the network model is evaluated and analyzed on three recognized retinal image datasets DRIVE, CHASE DB1, and STARE. The evaluation metrics were 0.9718, 0.9762, and 0.9773 for accuracy; 0.8410, 0.8437, and 0.8938 for sensitivity; and 0.8318, 0.8164, and 0.8557 for Dice coefficient. Compared with existing retinal image segmentation methods, the proposed method in this paper achieved better vessel segmentation in all of the publicly available fundus datasets tested performance and results.

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Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures

Cited by 22 publications

References 57 publications

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

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

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN

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