A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis

Raja, Chandrasekaran; Gangatharan, N.

doi:10.1016/j.compbiomed.2015.05.018

Cited by 39 publications

(19 citation statements)

References 47 publications

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“…Still, the data employed in the training were extracted from the images. The SVM classifier presented the most favorable results in ten of the papers selected, as in the research of the following authors: Acharya et al [39], Issac et al [40], Raja et al [45], Mohamed et al [50], and Rehman et al [51].…”

Section: Discussionmentioning

confidence: 98%

“…The segmentation step emerges as optional. Yet the preprocessing is performed in different approaches: image size reduction [38][39][40], image channels manipulation [30,45,50], histogram equalization and noise filtering [48], histogram of visual words [49] or bilateral filtering [51]. All the techniques were applied in order to highlight the OD and the OD-cup region.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, a valuable contribution of Acharya et al [39] was represented by the development of a risk index for glaucoma screening, which can be used in clinical daily activities. In addition, Raja et al [45] employed almost the same metrics as Acharya et al [39] with exception to skewness and kurtosis, extracted from the HWT.…”

Section: Discussionmentioning

confidence: 99%

“…In the same year, Raja et al [45] proposed a method based on the particle swarm optimization (PSO) and group search optimization (GSO). With the application of the PSO framework, the g-best values were extracted through the population.…”

Section: Methods Using Features Extraction For Glaucoma Diagnosismentioning

confidence: 99%

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Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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Introduction: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. Methods:The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized.Discussion: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques.Conclusions: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methods Using Features Extraction For Glaucoma Diagnosismentioning

confidence: 99%

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Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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“…It was concluded that all the machine learning techniques using combination of features have ability to detect 85 % glaucoma cases. An algorithm (Raja and Gangatharan 2015) used Wavelet transformation based features to discriminate glaucoma from healthy eyes. SVM classifier using radial basis function kernel was used.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated detection of glaucoma using structural and non structural features

et al. 2016

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Glaucoma is a chronic disease often called “silent thief of sight” as it has no symptoms and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural changes in the retina which aid ophthalmologists to detect glaucoma at an early stage and stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Our proposed technique provides a novel algorithm to detect glaucoma from digital fundus image using a hybrid feature set. This paper proposes a novel combination of structural (cup to disc ratio) and non-structural (texture and intensity) features to improve the accuracy of automated diagnosis of glaucoma. The proposed method introduces a suspect class in automated diagnosis in case of any conflict in decision from structural and non-structural features. The evaluation of proposed algorithm is performed using a local database containing fundus images from 100 patients. This system is designed to refer glaucoma cases from rural areas to specialists and the motivation behind introducing suspect class is to ensure high sensitivity of proposed system. The average sensitivity and specificity of proposed system are 100 and 87 % respectively.

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Retinopathy grading with deep learning and wavelet hyper-analytic activations

Raja¹,

Balaji

2022

Vis Comput

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Recent developments reveal the prominence of Diabetic Retinopathy (DR) grading. In the past few decades, Wavelet-based DR classification has shown successful impacts and the Deep Learning models, like Convolutional Neural Networks (CNN’s), have evolved in offering the highest prediction accuracy. In this work, the features of the input image are enhanced with the integration of Multi-Resolution Analysis (MRA) and a CNN framework without costing more convolution filters. The bottleneck with conventional activation functions, used in CNN’s, is the nullification of the feature maps that are negative in value. In this work, a novel Hyper-analytic Wavelet ( HW) phase activation function is formulated with unique characteristics for the wavelet sub-bands. Instead of dismissal, the function transforms these negative coefficients that correspond to significant edge feature maps . The hyper-analytic wavelet phase forms the imaginary part of the complex activation. And the hyper-parameter of the activation function is selected such that the corresponding magnitude spectrum produces monotonic and effective activations. The performance of 3 CNN models (1 custom, shallow CNN, ResNet with Soft attention, Alex Net for DR) with spatial–Wavelet quilts is better. With the spatial–Wavelet quilts, the Alex Net for DR has an improvement with an 11% of accuracy level (from 87 to 98%). The highest accuracy level of 98% and the highest Sensitivity of 99% are attained through Modified Alex Net for DR. The proposal also illustrates the visualization of the negative edge preservation with assumed image patches. From this study, the researcher infers that models with spatial–Wavelet quilts, with the hyper-analytic activations, have better generalization ability. And the visualization of heat maps provides evidence of better learning of the feature maps from the wavelet sub-bands.

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A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis

Cited by 39 publications

References 47 publications

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Automated detection of glaucoma using structural and non structural features

Retinopathy grading with deep learning and wavelet hyper-analytic activations

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