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Li Z, Xu M, Yang X, Han Y, Wang J. A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images. MICROMACHINES 2023; 14:705. [PMID: 36985112 PMCID: PMC10054796 DOI: 10.3390/mi14030705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/05/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-disease fundus image classification ensemble model based on gradient-weighted class activation mapping (Grad-CAM) is proposed. The model uses VGG19 and ResNet50 as the classification networks. Grad-CAM is a data augmentation module used to obtain a network convolutional layer output activation map. Both the augmented and the original data are used as the input of the model to achieve the classification goal. The data augmentation module can guide the model to learn the feature differences of lesions in the fundus and enhance the robustness of the classification model. Model fine tuning and transfer learning are used to improve the accuracy of multiple classifiers. The proposed method is based on the RFMiD (Retinal Fundus Multi-Disease Image Dataset) dataset, and an ablation experiment was performed. Compared with other methods, the accuracy, precision, and recall of this model are 97%, 92%, and 81%, respectively. The resulting activation graph shows the areas of interest for model classification, making it easier to understand the classification network.
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Li W, Xiao Y, Hu H, Zhu C, Wang H, Liu Z, Sangaiah AK. Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images. Front Public Health 2022; 10:914973. [PMID: 36159307 PMCID: PMC9500397 DOI: 10.3389/fpubh.2022.914973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/14/2022] [Indexed: 01/22/2023] Open
Abstract
Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.
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Affiliation(s)
- Wenjing Li
- Hunan Province People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yalong Xiao
- The College of Literature and Journalism, Central South University, Changsha, China,Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China
| | - Hangyu Hu
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
| | - Chengzhang Zhu
- The College of Literature and Journalism, Central South University, Changsha, China,Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China,*Correspondence: Chengzhang Zhu
| | - Han Wang
- Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China,School of Computer Science and Engineering, Central South University, Changsha, China
| | - Zixi Liu
- Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China,School of Computer Science and Engineering, Central South University, Changsha, China
| | - Arun Kumar Sangaiah
- School of Computing Science and Engineering, Vellore Institute of Technology University, Chennai, Tamil Nadu, India
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Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3585506. [PMID: 36072751 PMCID: PMC9441346 DOI: 10.1155/2022/3585506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/12/2022] [Accepted: 07/29/2022] [Indexed: 11/22/2022]
Abstract
This study develops an accurate method based on the generative adversarial network (GAN) that targets the issue of the current discontinuity of micro vessel segmentation in the retinal segmentation images. The processing of images has become increasingly efficient since the advent of deep learning method. We have proposed an improved GAN combined with SE-ResNet and dilated inception block for the segmenting retinal vessels (SAD-GAN). The GAN model has been improved with respect to the following points. (1) In the generator, the original convolution block is replaced with SE-ResNet module. Furthermore, SE-Net can extract the global channel information, while concomitantly strengthening and weakening the key features and invalid features, respectively. The residual structure can alleviate the issue of gradient disappearance. (2) The inception block and dilated convolution are introduced into the discriminator, which enhance the transmission of features and expand the acceptance domain for improved extraction of the deep network features. (3) We have included the attention mechanism in the discriminator for combining the local features with the corresponding global dependencies, and for highlighting the interdependent channel mapping. SAD-GAN performs satisfactorily on public retina datasets. On DRIVE dataset, ROC_AUC and PR_AUC reach 0.9813 and 0.8928, respectively. On CHASE_DB1 dataset, ROC_AUC and PR_AUC reach 0.9839 and 0.9002, respectively. Experimental results demonstrate that the generative adversarial model, combined with deep convolutional neural network, enhances the segmentation accuracy of the retinal vessels far above that of certain state-of-the-art methods.
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Yue C, Ye M, Wang P, Huang D, Lu X. SRV-GAN: A generative adversarial network for segmenting retinal vessels. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:9948-9965. [PMID: 36031977 DOI: 10.3934/mbe.2022464] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance. So, this paper proposes an improved GAN. Based on R2U-Net, the generator adds an attention mechanism, channel and spatial attention, which can reduce the loss of information and extract more effective features. We use dense connection modules in the discriminator. The dense connection module has the characteristics of alleviating gradient disappearance and realizing feature reuse. After a certain amount of iterative training, the generated prediction map and label map can be distinguished. Based on the loss function in the traditional GAN, we introduce the mean squared error. By using this loss, we ensure that the synthetic images contain more realistic blood vessel structures. The values of area under the curve (AUC) in the retinal blood vessel pixel segmentation of the three public data sets DRIVE, CHASE-DB1 and STARE of the proposed method are 0.9869, 0.9894 and 0.9885, respectively. The indicators of this experiment have improved compared to previous methods.
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Affiliation(s)
- Chen Yue
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Mingquan Ye
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Peipei Wang
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Daobin Huang
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Xiaojie Lu
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
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Xu Y, Fan Y. Dual-channel asymmetric convolutional neural network for an efficient retinal blood vessel segmentation in eye fundus images. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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