Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases

Panchal, Sachin; Naik, Ankita; Kokare, Manesh; Pachade, Samiksha; Naigaonkar, Rushikesh; Phadnis, Prerana; Bhange, Archana

doi:10.3390/data8020029

Cited by 14 publications

(8 citation statements)

References 50 publications

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“…Meanwhile, a hold-out test was conducted to evaluate the proposed approach on entirely new data, which had not been used in the training process. Thus, the RFMiD 2.0 data (Panchal et al, 2023 ) were exploited in the hold-out test Figure 6 . The corresponding experimental results are Kappa (0.681 ± 0.03), F1 score (0.927 ± 0.04), AUC (0.944 ± 0.08), and AVG (0.851 ± 0.03).…”

Section: Methodsmentioning

confidence: 99%

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Multi-label classification of retinal disease via a novel vision transformer model

Wang,

Lian,

Jiao

2024

Front. Neurosci.

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IntroductionThe precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously.MethodsThis study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function.Results and discussionThe experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG.

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Section: Methodsmentioning

confidence: 99%

“…CAMs generated using the proposed approach. (Panchal et al, 2023) were exploited in the hold-out test Figure 6. The corresponding experimental results are Kappa (0.681 ± 0.03), F1 score (0.927 ± 0.04), AUC (0.944 ± 0.08), and AVG (0.851 ± 0.03).…”

Section: Figurementioning

confidence: 99%

Multi-label classification of retinal disease via a novel vision transformer model

Wang,

Lian,

Jiao

2024

Front. Neurosci.

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“…The selected fundus images with optic disk edema from RFMiD public dataset 47 contained 91 edematous and 91 non-edematous ODs images with dimensions between 2144 × 1424 and 4288 × 2848. From the RFMiD2.0 public dataset 48 , 20 edematous and 20 non-edematous OD images with the dimensions 512 × 512 and 2048 × 1536 were selected. A total of 295 OD images with 146 edematous and 149 non-edematous cases were used in the experiments.…”

Section: Datasets Classifier and Evaluationmentioning

confidence: 99%

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

Naing,

Aimmanee

2024

Sci Rep

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One significant ocular symptom of neuro-ophthalmic disorders of the optic disk (OD) is optic disk edema (ODE). The etiologies of ODE are broad, with various symptoms and effects. Early detection of ODE can prevent potential vision loss and fatal vision problems. The texture of edematous OD significantly differs from the non-edematous OD in retinal images. As a result, techniques that usually work for non-edematous cases may not work well for edematous cases. We propose a fully automatic OD classification of edematous and non-edematous OD on fundus image collections containing a mixture of edematous and non-edematous ODs. The proposed algorithm involved localization, segmentation, and classification of edematous and non-edematous OD. The factorized gradient vector flow (FGVF) was used to segment the ODs. The OD type was classified using a linear support vector machine (SVM) based on 27 features extracted from the vessels, GLCM, color, and intensity line profile. The proposed method was tested on 295 images with 146 edematous cases and 149 non-edematous cases from three datasets. The segmentation achieves an average precision of 88.41%, recall of 89.35%, and F1-Score of 86.53%. The average classification accuracy is 99.40% and outperforms the state-of-the-art method by 3.43%.

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“…The available images allow for the creation of predictive ML models. Many image databases, such as the Retinal fundus multi-disease image dataset (RFMiD) database, specialize in high-resolution images [154]. Khan et al [155] have provided a systematic review of the available eye image databases.…”

Section: Data Governancementioning

confidence: 99%

miRNA Studies in Glaucoma: A Comprehensive Review of Current Knowledge and Future Perspectives

Dobrzycka,

Sulewska,

Biecek

et al. 2023

IJMS

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Glaucoma, a neurodegenerative disorder that leads to irreversible blindness, remains a challenge because of its complex nature. MicroRNAs (miRNAs) are crucial regulators of gene expression and are associated with glaucoma and other diseases. We aimed to review and discuss the advantages and disadvantages of miRNA-focused molecular studies in glaucoma through discussing their potential as biomarkers for early detection and diagnosis; offering insights into molecular pathways and mechanisms; and discussing their potential utility with respect to personalized medicine, their therapeutic potential, and non-invasive monitoring. Limitations, such as variability, small sample sizes, sample specificity, and limited accessibility to ocular tissues, are also addressed, underscoring the need for robust protocols and collaboration. Reproducibility and validation are crucial to establish the credibility of miRNA research findings, and the integration of bioinformatics tools for miRNA database creation is a valuable component of a comprehensive approach to investigate miRNA aberrations in patients with glaucoma. Overall, miRNA research in glaucoma has provided significant insights into the molecular mechanisms of the disease, offering potential biomarkers, diagnostic tools, and therapeutic targets. However, addressing challenges such as variability and limited tissue accessibility is essential, and further investigations and validation will contribute to a deeper understanding of the functional significance of miRNAs in glaucoma.

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Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases

Cited by 14 publications

References 50 publications

Multi-label classification of retinal disease via a novel vision transformer model

Multi-label classification of retinal disease via a novel vision transformer model

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

miRNA Studies in Glaucoma: A Comprehensive Review of Current Knowledge and Future Perspectives

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