NDLI: Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention

Content Provider	Springer Nature : BioMed Central
Author	Tan, Jiachen Dong, Yongquan Li, Junchi
Abstract	Fundus ultrasound image classification is a critical issue in the medical field. 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.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-023-01047-w.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-023-01047-w
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-07-06
Access Restriction	Open
Subject Keyword	Imaging Radiology Fundus ultrasound images Image classification Deep learning Siamese network
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

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