Benchmark data set for glaucoma detection with annotated cup to disc ratio

Salam, Anum Abdul; Akram, M. Usman; Arouj, Amna; Basit, Imran; Shaqur, Tariq; Javed, Haroon; Sheeraz,; Wazir, Kamran

doi:10.1109/icsigsys.2017.7967046

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Reviewing these publications revealed many studies that have proposed benchmark datasets, and in a few cases benchmark tasks that meet our definition (see Section 2.1 for this definition), across a wide array of medical disciplines. For example, there have been benchmark datasets proposed for peripheral blood cell recognition (Acevedo et al, 2020), brain tumor image segmentation (Menze et al, 2014), tuberculosis identification from X-ray images (Jaeger et al, 2014), cervical cytology analysis (Zhang et al, 2019), glaucoma detection (Salam et al, 2017), ischemic stroke lesion segmentation from MRI images (Maier et al, 2017), seizure detection (Harati et al, 2014), human activity sensing and motion assessment (Kawaguchi et al, 2011;Ebert et al, 2017), voice disorder detection (Cesari et al, 2018), demographic trait detection from clinical notes (Feder et al, 2020), biomedical knowledge link prediction (Breit et al, 2020), molecular machine learning (Wu et al, 2018), ECG interpretation (Wagner et al, 2020), ICU predictions such as mortality, length of stay, patient decline, and phenotyping (Harutyunyan et al, 2019;Purushotham et al, 2018;Sheikhalishahi et al, 2020), neurodegenerative disorder diagnosis (Tagaris et al, 2018), prostate cancer survival prediction (Guinney et al, 2017), and several tasks from the UCI machine learning repository such as predicting chronic kidney disease, diabetes, breast cancer and more (Dua and Graff, 2017).…”

Section: Resultsmentioning

confidence: 99%

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data

Bellamy,

Celi,

Beam

2020

Preprint

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The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled other sub-fields of machine learning forward at an equally impressive pace, but in healthcare it has primarily been image processing tasks, such as in dermatology and radiology, that have experienced similar benchmark-driven progress. In the present study, we performed a comprehensive review of benchmarks in medical machine learning for structured data, identifying one based on the Medical Information Mart for Intensive Care (MIMIC-III) that allows the first direct comparison of predictive performance and thus the evaluation of progress on four clinical prediction tasks: mortality, length of stay, phenotyping, and patient decompensation. We find that little meaningful progress has been made over a 3 year period on these tasks, despite significant community engagement. Through our meta-analysis, we find that the performance of deep recurrent models is only superior to logistic regression on certain tasks. We conclude with a synthesis of these results, possible explanations, and a list of desirable qualities for future benchmarks in medical machine learning.

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Section: Resultsmentioning

confidence: 99%

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data

Bellamy,

Celi,

Beam

2020

Preprint

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“…Moreover, a limited number of glaucoma classified datasets are publically available. Consequently, a comparison of different glaucoma detection algorithms is somewhat challenging [38]. Nevertheless, most related works report an accuracy that is usually within the range from 85 to 95% [12].…”

Section: Discussionmentioning

confidence: 99%

Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features

Abdel-Hamid

2019

J Digit Imaging

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Glaucoma is a silent progressive eye disease that is among the leading causes of irreversible blindness. Early detection and proper treatment of glaucoma can limit severe vision impairments associated with advanced stages of the disease. Periodic automatic screening can help in the early detection of glaucoma while reducing the workload on expert ophthalmologists. In this work, a wavelet-based glaucoma detection algorithm is proposed for real-time screening systems. A combination of wavelet-based statistical and textural features computed from the detected optic disc region is used to determine whether a retinal image is healthy or glaucomatous. Two public datasets having different resolutions were considered in the performance analysis of the proposed algorithm. An accuracy of 96.7% and area under receiver operating curve (AUC) of 94.7% were achieved for the highresolution dataset. Analysis of the wavelet-based statistical and textural features using three different methods showed their relevance for glaucoma detection. Furthermore, the proposed algorithm is shown to be suitable for real-time applications as it requires less than 3 s for processing the high-resolution retinal images.

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“…However, this approach did not involve pathologic retinal images that are affecting the optic disc overall accuracy. Salam et al [21] proposed a unique method to classify fundus images into three categories: glaucoma patients, suspicious specimens, and nonglaucoma images. e method was tested on a local dataset containing 100 fundus images of 26 glaucoma patients and 74 nonglaucoma patients.…”

Section: Computer-based Generic Methods To Detect Glaucomamentioning

confidence: 99%

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

Zafar

Aamir

Nawi

et al. 2022

Mobile Information Systems

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Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.

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Benchmark data set for glaucoma detection with annotated cup to disc ratio

Cited by 5 publications

References 17 publications

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data

Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

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