Classification of Ocular Diseases Employing Attention-Based Unilateral and Bilateral Feature Weighting and Fusion

He, Junjun; Li, Cheng; Ye, Jin; Wang, Shanshan; Qiao, Yu; Gu, Lixu

doi:10.1109/isbi45749.2020.9098525

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…J. He et al [ 4 ] proposed an attention-based feature-weighted fusion network, which extracts the features of both fundus images through ResNet and classifies them after the feature fusion module. The network can classify 8 types of fundus images with an accuracy of 0.934, but a lower kappa value indicates that more samples have been misclassified.…”

Section: Related Workmentioning

confidence: 99%

“…With the improvement of image classification network performance in the field of computer vision [ 4 , 5 , 6 ], fundus image classification tasks often include the classification of single diseases, such as DR, AMD, and glaucoma disease staging [ 7 , 8 , 9 ] and multi-disease fundus image classification [ 10 ]. Networks commonly used for fundus image classification include Alex Net, VGG Net, ResNet, and EfficientNet.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images

Yang

et al. 2023

Micromachines

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At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-disease fundus image classification ensemble model based on gradient-weighted class activation mapping (Grad-CAM) is proposed. The model uses VGG19 and ResNet50 as the classification networks. Grad-CAM is a data augmentation module used to obtain a network convolutional layer output activation map. Both the augmented and the original data are used as the input of the model to achieve the classification goal. The data augmentation module can guide the model to learn the feature differences of lesions in the fundus and enhance the robustness of the classification model. Model fine tuning and transfer learning are used to improve the accuracy of multiple classifiers. The proposed method is based on the RFMiD (Retinal Fundus Multi-Disease Image Dataset) dataset, and an ablation experiment was performed. Compared with other methods, the accuracy, precision, and recall of this model are 97%, 92%, and 81%, respectively. The resulting activation graph shows the areas of interest for model classification, making it easier to understand the classification network.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images

Yang

et al. 2023

Micromachines

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“…Multi-label learning problem is an important research direction in machine learning field. It has been widely used in image and video annotation [1] ; text classification [2] ; gene function prediction [3] ; clinical medicine [4] et al In Multi-label learning problem, each training sample can be labeled with multiple different labels at the same time. For example, in the image classification task, an image can have multiple label information at the same time.…”

Section: Introductionmentioning

confidence: 99%

Multi label image classification learning with weak labels

Lu,

Dou

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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A new multi-label similarity semantic learning(ML-SSL) model was proposed to solve the problem of label missing in existing multi-label image classification methods, It can produce better classification results by effectively recovering missing label information in training data. The model considers the characteristics of label structure and instance features, and recovers the missing label information in training data by using the label correlation within images and the similarity between images. After label recovery, the new training set is used to train the classification model, and the model is used to predict the test set. The experimental results show that the model has better performance improvement in image classification tasks under weak labeling.

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Classification of Ocular Diseases: A Vision Transformer-Based Approach

Gummadi,

Ghosh

2023

Innovations in Computational Intelligence and Computer Vision

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Classification of Ocular Diseases Employing Attention-Based Unilateral and Bilateral Feature Weighting and Fusion

Cited by 10 publications

References 6 publications

A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images

A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images

Multi label image classification learning with weak labels

Classification of Ocular Diseases: A Vision Transformer-Based Approach

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