ArticlePloS one2021
MHANet: A hybrid attention mechanism for retinal diseases classification.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy.Frontiers in medicine · 2026Article
- Automated retinal disease classification using deep learning and AlexNet with statistical models analysis.PloS one · 2026Article
- Distributed training of foundation models for ophthalmic diagnosis.Communications engineering · 2025Article
- WaveAttention-ResNet: a deep learning-based intelligent diagnostic model for the auxiliary diagnosis of multiple retinal diseases.Frontiers in radiology · 2025Article
- A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach.Bioengineering (Basel, Switzerland) · 2024Article
- A new computer-aided diagnosis tool based on deep learning methods for automatic detection of retinal disorders from OCT images.International ophthalmology · 2024Article
- A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification.Scientific reports · 2023Article
- Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules.BMC medical imaging · 2023Article
- Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.
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