ArticleJournal of imaging2023
Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.Journal of ovarian research · 2026Article
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- Capuchin Red Kite–optimized Swin Transformer-based Convolutional Block Attention Module for Early Diagnosis and Classification of PneumoniaCurrent medical imaging · 2026Article
- SSLVNet: an explainable multi-modal hybrid framework integrating self-supervised learning and vision transformers for multi-label classification of retinal diseases.Frontiers in artificial intelligence · 2026Article
- Leveraging Vision Transformers for High-Precision Classification of Cancer Cell Cultures: A Comparative Study on MDA-MB-231 and PC3 Datasets.IEEE open journal of engineering in medicine and biology · 2026Article
- Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation.Journal of imaging informatics in medicine · 2025Article
- Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.Journal of imaging informatics in medicine · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- A Comparative Study of Lesion-Centered and Severity-Based Approaches to Diabetic Retinopathy Classification: Improving Interpretability and Performance.Biomedicines · 2025Article
- Ensemble-based eye disease detection system utilizing fundus and vascular structures.Scientific reports · 2025Article
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- Convolutional variational auto-encoder and vision transformer hybrid approach for enhanced early Alzheimer's detection.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.Journal of healthcare informatics research · 2025Article
- Article
- HyReti-Net: hybrid retinal diseases classification and diagnosis network using optical coherence tomography.Frontiers in medicine · 2025Article
- MSLI-Net: retinal disease detection network based on multi-segment localization and multi-scale interaction.Frontiers in cell and developmental biology · 2025Article
- Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.PloS one · 2024Article
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
The current advancement towards retinal disease detection mainly focused on distinct feature extraction using either a convolutional neural network (CNN) or a transformer-based end-to-end deep learning (DL) model. The individual end-to-end DL models are capable of only processing texture or shape-based information for performing detection tasks. However, extraction of only texture- or shape-based features does not provide the model robustness needed to classify different types of retinal diseases. Therefore, concerning these two features, this paper developed a fusion model called 'Conv-ViT' to detect retinal diseases from foveal cut optical coherence tomography (OCT) images. The transfer learning-based CNN models, such as Inception-V3 and ResNet-50, are utilized to process texture information by calculating the correlation of the nearby pixel. Additionally, the vision transformer model is fused to process shape-based features by determining the correlation between long-distance pixels. The hybridization of these three models results in shape-based texture feature learning during the classification of retinal diseases into its four classes, including choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. The weighted average classification accuracy, precision, recall, and F1 score of the model are found to be approximately 94%. The results indicate that the fusion of both texture and shape features assisted the proposed Conv-ViT model to outperform the state-of-the-art retinal disease classification models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.