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Vitreous opacity (VO) and posterior vitreous detachment (PVD) are two common eye diseases, Now, the diagnosis of these two diseases mainly relies on manual identification by doctors. This method has the disadvantages of time-consuming and manual investment, so it is very meaningful to use computer technology to assist doctors in diagnosis. This paper is the first to apply the deep learning model to VO and PVD classification tasks. Convolutional neural network (CNN) is widely used in image classification. Traditional CNN requires a large amount of training data to prevent overfitting, and it is difficult to learn the differences between two kinds of images well. In this paper, we propose an end-to-end siamese convolutional neural network with multi-attention (SVK_MA) for automatic classification of VO and PVD fundus ultrasound images. SVK_MA is a siamese-structure network in which each branch is mainly composed of pretrained VGG16 embedded with multiple attention models. Each image first is normalized, then is sent to SVK_MA to extract features from the normalized images, and finally gets the classification result. Our approach has been validated on the dataset provided by the cooperative hospital. The experimental results show that our approach achieves the accuracy of 0.940, precision of 0.941, recall of 0.940, F1 of 0.939 which are respectively increased by 2.5%, 1.9%, 3.4% and 2.5% compared with the second highest model.<\/jats:p>","DOI":"10.1186\/s12880-023-01047-w","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T10:01:53Z","timestamp":1688637713000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention"],"prefix":"10.1186","volume":"23","author":[{"given":"Jiachen","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongquan","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"issue":"01","key":"1047_CR1","first-page":"131","volume":"36","author":"JS Liu","year":"2020","unstructured":"Liu JS, Bian HX. 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We confirm that all methods are carried out in accordance with relevant guidelines and regulations. The data were desensitized before the authors received them. Data received by the authors were completely anonymous and the research experiments were authorized by the Xuzhou No.1 People\u2019s Hospital. Ethics number: xyyll [2022]-XJSFX-058. Xuzhou No.1 People\u2019s Hospital ethics committee approved all the Experiment Approach of this study. We confirm that all subjects have given informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"89"}}