Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network

Ma, Lei; Xu, Qihang; Hong, Hanyu; Shi, Yu; Zhu, Ying; Wang, Lei

doi:10.1007/s10489-023-04949-y

Cited by 5 publications

(3 citation statements)

References 58 publications

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“…In the domain of diabetic retinopathy classification, Karthika and Durgadevi (2024) and Bala et al (2023) employed a fusion of deep learning and attention mechanisms, yielding commendable results. Ma et al (2023) approached diabetic retinopathy classification as a dual challenge, addressing both ordered regression and multi-class classification. Leveraging the transformer's feature extraction capabilities, the integration of category and ordinal supervision information led to enhanced classification accuracy and kappa values.…”

Section: Relate Workmentioning

confidence: 99%

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification

Fu,

Wei,

Chen

et al. 2024

Phys. Med. Biol.

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Object: The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination. Approach: First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model’s performance. Main results: Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods. Significance: In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

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Section: Relate Workmentioning

confidence: 99%

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification

Fu,

Wei,

Chen

et al. 2024

Phys. Med. Biol.

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“…The hybrid structure also utilizes images with a resolution of 256×256 as input, achieving an accuracy increase to 94%. In addition, the combination of ViT with CNN was also proposed and applied [ 29 , 30 ]. Sadeghzadeh et al [ 29 ] fused EfficientNet-B0 with Transformer and achieved state-of-the-art classification results using 224×224 images as input on EyePACS, APTOS, DDR, Messidor-1, and Messidor-2 datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Sadeghzadeh et al [ 29 ] fused EfficientNet-B0 with Transformer and achieved state-of-the-art classification results using 224×224 images as input on EyePACS, APTOS, DDR, Messidor-1, and Messidor-2 datasets. Ma et al [ 30 ] proposed a fusion network based on Transformer and CNN for the grading of DR, where DR grading was treated as a joint ordinal regression and multi-classification problem. They utilized images with a slightly higher resolution of 384×384 and demonstrated superior performance on the DeepDR and IDRiD datasets.…”

Section: Related Workmentioning

confidence: 99%

Vision transformer with masked autoencoders for referable diabetic retinopathy classification based on large-size retina image

Yang,

Cai,

Qiu

et al. 2024

PLoS ONE

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Computer-aided diagnosis systems based on deep learning algorithms have shown potential applications in rapid diagnosis of diabetic retinopathy (DR). Due to the superior performance of Transformer over convolutional neural networks (CNN) on natural images, we attempted to develop a new model to classify referable DR based on a limited number of large-size retinal images by using Transformer. Vision Transformer (ViT) with Masked Autoencoders (MAE) was applied in this study to improve the classification performance of referable DR. We collected over 100,000 publicly fundus retinal images larger than 224×224, and then pre-trained ViT on these retinal images using MAE. The pre-trained ViT was applied to classify referable DR, the performance was also compared with that of ViT pre-trained using ImageNet. The improvement in model classification performance by pre-training with over 100,000 retinal images using MAE is superior to that pre-trained with ImageNet. The accuracy, area under curve (AUC), highest sensitivity and highest specificity of the present model are 93.42%, 0.9853, 0.973 and 0.9539, respectively. This study shows that MAE can provide more flexibility to the input image and substantially reduce the number of images required. Meanwhile, the pretraining dataset scale in this study is much smaller than ImageNet, and the pre-trained weights from ImageNet are not required also.

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DDABNet: a dense Do-conv residual network with multisupervision and mixed attention for image deblurring

Shi,

Huang,

Chen

et al. 2023

Appl Intell

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Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network

Cited by 5 publications

References 58 publications

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification

Vision transformer with masked autoencoders for referable diabetic retinopathy classification based on large-size retina image

DDABNet: a dense Do-conv residual network with multisupervision and mixed attention for image deblurring

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