融合注意力谱非局部块的视网膜图像质量分级

doi:10.19665/j.issn1001-2400.20231101

Abstract

Abstract:

Retinal image quality assessment(RIQA) is one of the key components of screening for diabetic retinopathy.Aiming at the problems of large differences in retinal image quality and insufficient generalization ability of quality evaluation models,a multi-feature algorithm that combines non-local blocks of the attention spectrum is proposed to predict and rank RIQA.First,the ResNet50 network of fused spectral non-local blocks is used to extract the features of the input images;Second,efficient channel attention is introduced to improve the model's ability to express data and effectively capture the characteristic information relationship between channels;Then,the feature iterative attention fusion module is used to fuse the local feature information.Finally,the combined focus loss and regular loss further improve the effect of quality classification.On the Eye-Quality dataset,the accuracy rate is 88.59%,the precision is 87.56%,the sensitivity and F1 value are 86.10% and 86.74%,respectively.The accuracy and F1 values on the RIQA-RFMiD dataset are 84.22% and 67.17%,respectively,and simulation experiments show that the proposed algorithm has a good generalization ability for retinal image quality assessment tasks.

Key words: retinal image quality grading, spectral non-local blocks, attention mechanisms, feature iterative fusion, combined losses

CLC Number:

TP391

LIANG Liming, DONG Xin, LEI Kun, XIA Yuchen, WU Jian. Retinal image quality grading for fused attention spectrum non-local blocks[J].Journal of Xidian University, 2024, 51(4): 102-113.

Figures/Tables 12

References 25

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实验	准确率/%	精确率/%	召回率/%	F1/%
M1	88.05	86.70	85.82	86.20
M2	88.08	86.57	85.72	86.13
M3	88.28	87.04	85.80	86.30
M4	88.47	87.77	85.64	86.61
M5	88.59	87.56	86.10	86.74

模型	准确率/%	精确率/%	召回率/%	F1/%
NBIQA^[22]	79.17	76.41	75.09	74.41
TS-CNN^[23]	79.26	79.76	74.46	74.81
DenseNet121-RGB^[7]	85.68	84.81	82.39	83.15
DenseNet121-MCF^[7]	87.22	85.63	84.82	85.06
ResNeSt^[24]	87.35	85.87	85.07	85.44
Dual-path CNN^[9]	—	88.68	87.86	88.20
文中方法	88.59	87.56	86.10	86.74

模型	准确率/%	精确率/%	召回率/%	F1/%
ResNet50^[25]	83.75	77.84	56.49	61.84
DenseNet121-RGB^[7]	83.13	71.87	57.41	60.42
DenseNet121-MCF^[7]	82.81	75.13	61.31	65.53
文中算法	84.22	73.95	62.94	67.17

Retinal image quality grading for fused attention spectrum non-local blocks

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