Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion
Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/13/6/947 |
| _version_ | 1797484284922036224 |
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| author | Zhenwei Li Mengying Xu Xiaoli Yang Yanqi Han |
| author_facet | Zhenwei Li Mengying Xu Xiaoli Yang Yanqi Han |
| author_sort | Zhenwei Li |
| collection | DOAJ |
| description | Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed features in binocular fundus images, and then feeds them into a ResNet50 network with attention mechanisms to extract fundus image lesion features. The model obtains global features of binocular images through feature fusion and uses Softmax to classify multi-label fundus images. The ODIR binocular fundus image dataset was used to evaluate the network classification performance and conduct ablation experiments. The model’s backend is the Tensorflow framework. Through experiments on the test images, this method achieved accuracy, precision, recall, and <i>F</i>1 values of 94.23%, 99.09%, 99.23%, and 99.16%, respectively. |
| first_indexed | 2024-03-09T23:01:20Z |
| format | Article |
| id | doaj.art-b38e55724dce4350b1dfcf7161891c1b |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T23:01:20Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-b38e55724dce4350b1dfcf7161891c1b2023-11-23T18:02:03ZengMDPI AGMicromachines2072-666X2022-06-0113694710.3390/mi13060947Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature FusionZhenwei Li0Mengying Xu1Xiaoli Yang2Yanqi Han3College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471032, ChinaCollege of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471032, ChinaCollege of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471032, ChinaCollege of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471032, ChinaFundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed features in binocular fundus images, and then feeds them into a ResNet50 network with attention mechanisms to extract fundus image lesion features. The model obtains global features of binocular images through feature fusion and uses Softmax to classify multi-label fundus images. The ODIR binocular fundus image dataset was used to evaluate the network classification performance and conduct ablation experiments. The model’s backend is the Tensorflow framework. Through experiments on the test images, this method achieved accuracy, precision, recall, and <i>F</i>1 values of 94.23%, 99.09%, 99.23%, and 99.16%, respectively.https://www.mdpi.com/2072-666X/13/6/947attention mechanismsdeep learningfeature fusionimage classificationfundus images |
| spellingShingle | Zhenwei Li Mengying Xu Xiaoli Yang Yanqi Han Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion Micromachines attention mechanisms deep learning feature fusion image classification fundus images |
| title | Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion |
| title_full | Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion |
| title_fullStr | Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion |
| title_full_unstemmed | Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion |
| title_short | Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion |
| title_sort | multi label fundus image classification using attention mechanisms and feature fusion |
| topic | attention mechanisms deep learning feature fusion image classification fundus images |
| url | https://www.mdpi.com/2072-666X/13/6/947 |
| work_keys_str_mv | AT zhenweili multilabelfundusimageclassificationusingattentionmechanismsandfeaturefusion AT mengyingxu multilabelfundusimageclassificationusingattentionmechanismsandfeaturefusion AT xiaoliyang multilabelfundusimageclassificationusingattentionmechanismsandfeaturefusion AT yanqihan multilabelfundusimageclassificationusingattentionmechanismsandfeaturefusion |