Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images

Haider, Adnan; Arsalan, Muhammad; Lee, Min Beom; Owais, Muhammad; Mahmood, Tahir; Sultan, Haseeb; Park, Kang Ryoung

doi:10.1016/j.eswa.2022.117968

Cited by 38 publications

(17 citation statements)

References 47 publications

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“…The RMSDSC-Net outperforms the recent results by Almubarak et al 32 in DC for OC and OD by (1.5%, 5.5%) on the REFUGE database and Hervella et al 46 in DC for OC and OD by (1.8%, 1.7%) on the DRISHTI-GS database. Finally, we have compared the results with the latest papers from Haider et al, 43 Sun et al, 47 and Hervella et al 48 The proposed approach outperforms the earlier best results by Sun et al 47 on OC DC by around 2.1%. Also, it performs better than the recent best results by Hervella et al 48 and Adnan et al 43 on OC DC on the DRISHTI-GS and REGUFE databases.…”

Section: Discussion and Comparison With The State-of-the-art Approachesmentioning

confidence: 88%

“…Furthermore, to reflect the high efficiency of the proposed model, this paper compares the training time with the most related models, including U‐Net 24 and SLSR‐Net, 43 on the DRISHTI‐GS and REFUGE databases, as illustrated in Table 5. In Table 5, we give the training time of each image and the total time of the training process obtained by all the comparison approaches.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…To verify the effectiveness of the proposed approach, we compare our approach with the state‐of‐the‐art approaches, including U‐Net, 24 M‐Net, 25 FC‐DenseNet, 26 pOSAL, 29 Conditional Generative Adversarial Network (GAN), 30 Two‐stage Mask R‐CNN, 32 Team BUCT, 41 Stack‐U‐Net, 44 Wasserstein Generative Adversarial Network (WGAN), 45 Multimodal, 46 ResFPN‐Net, 47 M‐Ada, 48 and SLSR‐Net 43 . Tables 7 and 8 depict the OD and OC segmentation results for different approaches on the DRISHTI‐GS and REFUGE databases, respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

Zhou

Yuhan

et al. 2022

Int J of Intelligent Sys

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Glaucoma is an eye disease that leads to irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation can effectively facilitate ophthalmologist in glaucoma diagnosis. Recently, a series of deep learning approaches attain promising performance in OD and OC segmentation but still face the challenge to precisely segment OC boundary with enhanced computational efficiency. To address this issue, we propose a novel network named Robust Multiscale Feature Extraction with Depthwise Separable Convolution (RMSDSC‐Net), which can better solve the challenging tradeoff between segmentation performance and network cost. The proposed RMSDSC‐Net is mainly composed of Multiscale Input (MSI), Depthwise Separable Convolution Unit (DSCU), Dilated Convolution Block (DCB), and External Residual Connection (ERC). First, the introduction of MSI can reduce the information loss due to the pooling layers used in the network for capturing rich feature representations. Next, to enhance segmentation performance and computational efficiency, this paper designs DSCU and DCB modules to avoid spatial information loss from minor details of the image and preserve more high‐level semantic features. Finally, this paper develops ERC established between the encoding layers and decoding layers to minimize the feature degradation problem. Hence, a high segmentation performance can be achieved using a shallow network. To evaluate the performance of the proposed network, extensive experiments have been enforced on two publicly available databases, DRISHTI‐GS and REFUGE. Our approach outperforms the state‐of‐the‐art approaches with the Dice Coefficient of (0.978, 0.919) and (0.965, 0.910) for OD and OC segmentation on DRISHTI‐GS and REFUGE databases, respectively. As a result, the proposed approach has a strong potential in analyzing fundus images for glaucoma diagnosis.

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Section: Discussion and Comparison With The State-of-the-art Approachesmentioning

confidence: 88%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

Zhou

Yuhan

et al. 2022

Int J of Intelligent Sys

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“…Haider et al [26] proposed two networks, the separable linked segmentation network (SLS-Net) and the separable linked segmentation residual network (SLSR-Net), for accurate pixelwise segmentation of the OC and OD in retinal fundus photographs. SLS-Net and SLSR-Net improve the OC and OD segmentation efficiency via minimization of the spatial information loss.…”

Section: B Deep Domain Adaptation In Glaucoma Researchmentioning

confidence: 99%

Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

Madadi¹,

Abu-Serhan²,

Yousefi³

2022

Preprint

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<p>Domain adaptation methods are designed to extract shared domain-invariant features by projecting data on a common subspace in order to align their domain distributions. However, these methods do not usually consider domain-specific features, and therefore their distributions may not be well aligned. To address this problem, we introduce a novel model that learns domain-invariant and domain-specific representations to extract both their general and specific features. We also propose progressive weighting to accurately transfer the source domain knowledge and to mitigate negative knowledge transfer from the source to the target domain and to employ low-rank coding for aligning the source and target distributions. We evaluated the model based on several real-world datasets and showed that our method significantly improved the accuracy for forecasting and diagnosis of glaucoma disease from fundus photographs.</p>

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“…In recent years, artificial intelligence has made significant contributions to automating manual processes (Arsalan et al, 2022c;Haider et al, 2022a;Mahmood et al, 2022b). In particular, the combination of deep learning with computer vision has enabled complex problems to be solved using multimedia-based learning (Owais et al, 2021;Sultan et al, 2021;Arsalan et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Robust segmentation of underwater fish based on multi-level feature accumulation

Haider

Arsalan²,

Choi³

et al. 2022

Front. Mar. Sci.

Self Cite

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Because fish are vital to marine ecosystems, monitoring and accurate detection are crucial for assessing the potential for fisheries in these environments. Conventionally, fish-related assessment is conducted manually, which makes it labor-intensive and time-consuming. In addition, the assessments are challenging owing to underwater visibility limitations, which leads to poor detection accuracy. To overcome these problems, we propose two novel architectures for the automatic and high-performance segmentation of fish populations. In this study, the efficient fish segmentation network (EFS-Net) and multi-level feature accumulation-based segmentation network (MFAS-Net) are the base and final networks, respectively. In deep convolutional neural networks, the initial layers usually contain potential spatial information. Therefore, the EFS-Net employs a series of convolution layers in the early stage of the network for optimal feature extraction. To boost segmentation accuracy, the MFAS-Net uses an initial feature refinement and transfer block to refine potential low-level information and subsequently transfers it to the deep stages of the network. Moreover, the MFAS-Net employs multi-level feature accumulation that improves pixel-wise prediction for fish that are indistinct. The proposed networks are evaluated using two publicly available datasets, namely DeepFish and semantic segmentation of underwater imagery (SUIM), both of which contain challenging underwater fish segmentation images. The experimental results reveal that mean intersection-over-unions of 76.42% and 92.0% are attained by the proposed method for the DeepFish and SUIM datasets, respectively; these values are higher than those by the state-of-the-art methods such as A-LCFCN+PM and DPANet. In addition, high segmentation performance is achieved without compromising the computational efficiency of the networks. The MFAS-Net requires only 3.57 million trainable parameters to be fully trained. The proposed model and the complete code will be made available1.

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Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images

Cited by 38 publications

References 47 publications

RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

Robust segmentation of underwater fish based on multi-level feature accumulation

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