Automated quality classification of colour fundus images based on a modified residual dense block network

Zhang, Fang; Xu, Xu; Xiao, Zhitao; Wu, Jun; Geng, Lei; Wang, Wen; Liu, Yanbei

doi:10.1007/s11760-019-01544-y

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…Experiments on the DR1 and MESSIDOR public datasets indicated that knowledge learned in other large datasets (source domain) could be better classified in small datasets (target domain) via transfer learning. 25 developed an enhanced residual dense block CNN, which could effectively classify fundus images into “good quality” and “low quality” to avoid delaying patient treatment and solve the problem of quality classification of fundus images. 26 offered a six-level cataract grading method that focuses on multifeature fusion and extracted features from the residual network (ResNet-18) and gray-level cooccurrence matrix (GLCM), with promising results.…”

Section: Related Workmentioning

confidence: 99%

Combining convolutional neural networks and self-attention for fundus diseases identification

Wang

et al. 2023

Sci Rep

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Early detection of lesions is of great significance for treating fundus diseases. Fundus photography is an effective and convenient screening technique by which common fundus diseases can be detected. In this study, we use color fundus images to distinguish among multiple fundus diseases. Existing research on fundus disease classification has achieved some success through deep learning techniques, but there is still much room for improvement in model evaluation metrics using only deep convolutional neural network (CNN) architectures with limited global modeling ability; the simultaneous diagnosis of multiple fundus diseases still faces great challenges. Therefore, given that the self-attention (SA) model with a global receptive field may have robust global-level feature modeling ability, we propose a multistage fundus image classification model MBSaNet which combines CNN and SA mechanism. The convolution block extracts the local information of the fundus image, and the SA module further captures the complex relationships between different spatial positions, thereby directly detecting one or more fundus diseases in retinal fundus image. In the initial stage of feature extraction, we propose a multiscale feature fusion stem, which uses convolutional kernels of different scales to extract low-level features of the input image and fuse them to improve recognition accuracy. The training and testing were performed based on the ODIR-5k dataset. The experimental results show that MBSaNet achieves state-of-the-art performance with fewer parameters. The wide range of diseases and different fundus image collection conditions confirmed the applicability of MBSaNet.

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Section: Related Workmentioning

confidence: 99%

Combining convolutional neural networks and self-attention for fundus diseases identification

Wang

et al. 2023

Sci Rep

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“…Some related results have been published on fundus image classification. In order not to delay the treatment of patients and to solve the quality classification of fundus images, Zhang et al proposed an improved residual dense block convolutional neural network to effectively divide fundus images into "good quality" and "poor quality" [30]. Zhang et al described a six-level cataract grading method focusing on multi-feature fusion, which extracted features from residual network (ResNet-18) and gray level co-occurrence matrix (GLCM), the results show advanced performance [31].…”

Section: Fundus Images Recognitionmentioning

confidence: 99%

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

et al. 2021

Preprint

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In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is timeconsuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

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“…Zago et al [16] used a pretrained model of the ImageNet database to extract general features and evaluate the classification performance of retinal quality by fine-tuning the weight parameters. Zhang et al [17] combined residual and dense network blocks to obtain more detailed features of fundus images. Chalakkal et al [18] proposed a two-stage evaluation method.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al. [17] combined residual and dense network blocks to obtain more detailed features of fundus images. Chalakkal et al.…”

Section: Related Workmentioning

confidence: 99%

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Blood vessel and background separation for retinal image quality assessment

Liu

et al. 2021

IET Image Processing

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Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end‐to‐end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U‐Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1‐score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1‐score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state‐of‐the‐art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.

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Automated quality classification of colour fundus images based on a modified residual dense block network

Cited by 13 publications

References 23 publications

Combining convolutional neural networks and self-attention for fundus diseases identification

Combining convolutional neural networks and self-attention for fundus diseases identification

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

Blood vessel and background separation for retinal image quality assessment

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