Deep membrane systems for multitask segmentation in diabetic retinopathy

Xue, Jie; Yan, Shuo; Qu, Jianhua; Feng, Qinghua; Qiu, Chenggong; Zhang, Hongyan; Chen, Meirong; Liu, Tingting; Li, Dengwang; Liu, Xiyu

doi:10.1016/j.knosys.2019.104887

Cited by 58 publications

(36 citation statements)

References 33 publications

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“…Xie et al [21] present in fundus images the original deformable-model-based algorithm for the complete mechanized recognition of the optical disk boundaries. The changes made for occlusions of the blood vessels in the model based algorithm are not quite reliable and stable.…”

Section: Related Workmentioning

confidence: 99%

A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

Akbar¹,

Midhunchakkaravarthy²

2020

RIA

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Image thresholding-based segmentation models play a vital role in the detection of Diabetic retinopathy (DR) on large databases. Most of the conventional segmentation-based classification models are independent of over segmented regions and outliers. Also, these models have less true positive rate and high error rate on different DR feature sets. In order to overcome these problems, a novel filtered based segmentation framework is designed and implemented on the large DR feature space. In this work, a novel image filtering approach, optimal image segmentation approach and hybrid Bayesian deep learning framework are developed on the large DR image databases. Experimental results proved that the proposed filtered segmentation-based Bayesian deep neural network has better accuracy and runtime than the conventional models on different DR variation databases.

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Section: Related Workmentioning

confidence: 99%

A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

Akbar¹,

Midhunchakkaravarthy²

2020

RIA

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“…Generally, there are three main families: cell-like P system [30], tissue-like P system [31,32] and neural-like P system [33]. The structure of the tissue-like P system can be viewed as a net, a tissue-like membrane system of degree m > 0 is constructed as follows [34]:…”

Section: Tissue-like and Cell-like Membrane Systemsmentioning

confidence: 99%

“…are initial multisets of objects present in cell i; R i are finite sets of evolution rules in cell i(1 ≤ i ≤ q), R is a finite set of communication rules; i 0 ∈ {0, 1, ..., q} indicates the output region where the computation results are placed.A cell-like P system has a hierarchical arrangement of membranes inside a skin membrane. Each membrane delimits a region where multisets of objects and rules are placed and a set of evolution rules take the form [ω → ω ][34].…”

mentioning

confidence: 99%

Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

2020

Self Cite

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Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.

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“…Patients suffering from diabetes, in some cases, lose their eyesight, which is often because of diabetic retinopathy (DR), though, there are some other causes of vision loss or poor eyesight, including other retinal and nonretina vision conditions such as macular degeneration and glaucoma (which are associated with age) and neuropathy vascular vision (which is Nonarteritic Anterior Ischemic Optic Neuropathy (NAION)) and cataract [1][2][3][4]. In addition, when an individual (a patient) with diabetes complains of visual disturbance, despite the 6.6's precision, the refractive errors, contrast sensitivity, direct light, and compliance range need to be taken into account.…”

Section: Introductionmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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Deep membrane systems for multitask segmentation in diabetic retinopathy

Cited by 58 publications

References 33 publications

A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

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