Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm

Ibrahim, Mohammed H.; Hacıbeyoğlu, Mehmet; Agaoglu, Afşin; Uçar, Fikret

doi:10.1007/s11517-022-02510-6

Cited by 14 publications

(4 citation statements)

References 37 publications

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“…All three methods work well. When controlling slopes, they can be used in combination to achieve better results 19 .…”

Section: Prediction Methods Of Slope Stability Coefficient Of Open Pi...mentioning

confidence: 99%

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Wang¹,

Zhang²,

Wang³

2023

Sci Rep

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The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development.

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“…All three methods work well. When controlling slopes, they can be used in combination to achieve better results 19 .…”

Section: Prediction Methods Of Slope Stability Coefficient Of Open Pi...mentioning

confidence: 99%

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Wang¹,

Zhang²,

Wang³

2023

Sci Rep

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“…Ibrahim et al [34] proposed a deep learning based decision support system for glaucoma disease detection. The proposed system comprises two phases; in the first phase, the normalization and mean absolute deviation technique was employed to preprocess the fundus images.…”

Section: Glaucoma Prediction Algorithmmentioning

confidence: 99%

Early-stage Glaucoma Disease Prediction Using Ensemble Optimized Deep Learning Classifier Model

2023

IJIES

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Nowadays, retinal fundus images (RFI) play a crucial role in the early and exact prediction of glaucoma. Glaucoma is a vision-threatening eye illness that primarily impairs the eye nerves, and the serious condition of glaucoma leads to permanent vision loss. Several computer vision-based techniques have been explored in past studies for glaucoma detection, but those have certain misclassification results and are not much focused on early stage detection. This work presents an ensemble deep network (Ensemble DeepNet) model for the early detection of glaucoma via retinal fundus images. Initially, the kernel bilateral filter (kernel BF) is employed to improve the clarity of retinal images and reduces image noise. Then, the brightness of the input image is modified using linear histogram transformation (LHT). Further, the segmentation of the optic disc (OD) and optic cup (OC) is performed using twostage thresholds U-network (Two-stage TUNet). Finally, a new ensemble DeepNet model is developed based on VGGNet, DenseNet, and CapsNet models using a weighted majority voting technique. The improved aquila optimization (IAO) algorithm is used to find the optimal weights for the ensemble DeepNet model. The proposed ensemble DeepNet model attains the highest of 99.45%, which shows that the proposed ensemble model is highly suitable for predicting glaucoma disease in an early stage.

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“…Kim et al ( 2018 ) and Guo et al ( 2020 ) diagnosed and localized fundus images by VGG16 and UNet++ networks to classify glaucoma and achieved an accuracy of 91.2% and an area under the curve (AUC) of 90.1%, respectively. Bajwa et al ( 2020 ) and Ibrahim et al ( 2022 ) both proposed a two-stage framework: the former detected and located optic disks on fundus images and then classified them as healthy or glaucoma; the latter preprocessed glaucoma disease data by normalization and the mean absolute deviation method in the first stage and trained a deep learning model through the artificial algae optimization algorithm later. They achieved an AUC of 87.4% and an F1 score of 98.15%.…”

Section: Background and Related Workmentioning

confidence: 99%

A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning

Zhang

Qian

et al. 2022

Front. Neurosci.

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Glaucoma is an optic neuropathy that leads to characteristic visual field defects. However, there is no cure for glaucoma, so the diagnosis of its severity is essential for its prevention. In this paper, we propose a multimodal classification architecture based on deep learning for the severity diagnosis of glaucoma. In this architecture, a gray scale image of the visual field is first reconstructed with a higher resolution in the preprocessing stage, and more subtle feature information is provided for glaucoma diagnosis. We then use multimodal fusion technology to integrate fundus images and gray scale images of the visual field as the input of this architecture. Finally, the inherent limitation of convolutional neural networks (CNNs) is addressed by replacing the original classifier with the proposed classifier. Our architecture is trained and tested on the datasets provided by the First Affiliated Hospital of Kunming Medical University, and the results show that the proposed architecture achieves superior performance for glaucoma diagnosis.

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Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm

Cited by 14 publications

References 37 publications

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Early-stage Glaucoma Disease Prediction Using Ensemble Optimized Deep Learning Classifier Model

A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning

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