Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images (Preprint)

Wan, Cheng; Hua, Rongrong; Li, Kunke; Hong, Xiangqian; Dong, Fang; Yang, Weihua

doi:10.2196/preprints.42463

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…Tird, because of the diferences of symptoms in diferent situations [47,48], age, gender, family genetic history, and other factors should be considered when building the model to improve the accuracy of the algorithm for diferent categories of RVO. Finally, for the lesion area, the analysis of fundus images with wavelet transforms and the extraction of spectral characteristics of bleeding point areas and edges can further improve efciency [49].…”

Section: Discussionmentioning

confidence: 99%

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Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images

Wan,

Hua,

et al. 2023

International Journal of Intelligent Systems

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Retinal vein occlusion (RVO) is the second common cause of blindness following diabetic retinopathy. The manual screening of fundus images to detect RVO is time consuming. Deep-learning techniques have been used for screening RVO due to their outstanding performance in many applications. However, unlike other images, medical images have smaller lesions, which require a more elaborate approach. To provide patients with an accurate diagnosis, followed by timely and effective treatment, we developed an intelligent method for automatic RVO screening on fundus images. Swin Transformer learns the hierarchy of low-to high-level features like the convolutional neural network. However, Swin Transformer extracts features from fundus images through attention modules, which pay more attention to the interrelationship between the features and each other. The model is more universal, does not rely entirely on the data itself, and focuses not only on local information but has a diffusion mechanism from local to global. To suppress overfitting, we adopt a regularization strategy, label smoothing, which uses one-hot to add noise to reduce the weight of the categories of true sample labels when calculating the loss function. The choice of different models using a 5-fold cross-validation on our own datasets indicates that Swin Transformer performs better. The accuracy of classifying all datasets is 98.75 ± 0.000, and the accuracy of identifying MRVO, CRVO, BRVO, and normal, using the method proposed in the paper, is 94.49 ± 0.094, 99.98 ± 0.015, 98.88 ± 0.08, and 99.42 ± 0.012, respectively. The method will be useful to diagnose RVO and help decide grade through fundus images, which has the potency to provide patients with further diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

“…Te preprint "Automatic Diagnosis of Diferent Types of Retinal Vein Occlusion Based on Fundus Images" [49] of this article is already published.…”

Section: Abbreviationsmentioning

confidence: 99%

Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images

Wan,

Hua,

et al. 2023

International Journal of Intelligent Systems

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“…The main fundus changes on color fundus photographs (CFPs) of patients with RVO include retinal hemorrhage, abnormal tortuous dilatation of retinal vessels, cotton velvet spots, and hard exudation [5][6] . The disease can also cause a variety of eye complications, such as retinal macular edema (RME), optic neuropathy, neovascular glaucoma, and traction retinal detachment [3][4][7][8][9] . RME is the main cause of severe visual impairment in patients with RVO [10] .…”

Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

Ji,

Liu,

Xie

et al. 2024

Int J Ophthalmol

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AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

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Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images (Preprint)

Cited by 2 publications

References 39 publications

Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images

Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

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