Application of deep learning for retinal image analysis: A review

Badar, Maryam; Haris, Muhammad; Fatima, Anam

doi:10.1016/j.cosrev.2019.100203

Cited by 140 publications

(53 citation statements)

References 71 publications

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“…From the observation of red, green, and blue components for monochromatic fundus photograph in previous studies [36,37], green light provides the best overall view of the retina and displays excellent contrast because the retinal pigmentations reflect green light more than blue light. Hence, green filter is utilized for enhancing the visibility of retinal vasculature, drusen, hemorrhage, and exudate.…”

Section: Resultsmentioning

confidence: 98%

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Pao

Lin

Chien

et al. 2020

Journal of Ophthalmology

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Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.

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Section: Resultsmentioning

confidence: 98%

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Pao

Lin

Chien

et al. 2020

Journal of Ophthalmology

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“…Nalepa and Kawulok in 2019 performed an extensive survey on existing techniques and methods to select SVM training data from large datasets and concluded that the DL will be more efficient than SVM for large datasets [41]. Badar et al show how to apply DL in Retina image classification and identification to detect diseases such as diabetic retinopathy, macular bunker, age-related macular degeneration, retinal detachment, retinoblastoma, and retinitis pigmentosa [42]. Yan et al in 2019 proposed a novel hybrid CNN and RNN for breast cancer image classification by using the richer multilevel feature representation of the histopathological biomedical image patches [43].…”

Section: A Survey Of Biomedical Imagementioning

confidence: 99%

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Tchapga

Mih

Kouanou

et al. 2021

Journal of Healthcare Engineering

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In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow.

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“…The resulting tools have been used to support diagnosis, by identifying similar patterns in images from patients who have not yet been diagnosed. There is evidence that tools can accurately identify some conditions, although at present it is still best to rely on a combination of tools and humans (Badar et al, 2020). Some of the excitement about machine learning concerns the prospects for finding novel patterns inand being able to make inferences from -personal datasets (Selbst et al, 2019;Malik, 2020).…”

Section: Machine Learning Tools: Materiality and Governancementioning

confidence: 99%

Machine learning, materiality and governance: A health and social care case study

Keen

Ruddle

Palczewski

et al. 2021

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There is a widespread belief that machine learning tools can be used to improve decision-making in health and social care. At the same time, there are concerns that they pose threats to privacy and confidentiality. Policy makers therefore need to develop governance arrangements that balance benefits and risks associated with the new tools. This article traces the history of developments of information infrastructures for secondary uses of personal datasets, including routine reporting of activity and service planning, in health and social care. The developments provide broad context for a study of the governance implications of new tools for the analysis of health and social care datasets. We find that machine learning tools can increase the capacity to make inferences about the people represented in datasets, although the potential is limited by the poor quality of routine data, and the methods and results are difficult to explain to other stakeholders. We argue that current local governance arrangements are piecemeal, but at the same time reinforce centralisation of the capacity to make inferences about individuals and populations. They do not provide adequate oversight, or accountability to the patients and clients represented in datasets.

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Application of deep learning for retinal image analysis: A review

Cited by 140 publications

References 71 publications

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Machine learning, materiality and governance: A health and social care case study

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