Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis

Liao, Wangmin; Zou, Beiji; Zhao, Rongchang; Chen, Yuanqiong; He, Zhiyou; Zhou, Mengjie

doi:10.1109/jbhi.2019.2949075

Cited by 105 publications

(54 citation statements)

References 26 publications

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“…In terms of AUC, and SE our method showed significant performance gain than all the comparative approaches. Liao et al [70] also reported the performance of their method using D c measure, where our method also outperformed their work. Moreover, our technique can easily run on CPU or GPU machines and each image test time is 0.9 s which is faster than the work of Ramani et al [39], which take 1.49 s. Hence, based on the result it can be concluded that our method is also equally reliable for the glaucoma detection.…”

Section: Comparative Studiesmentioning

confidence: 51%

“…Hence, the performance comparison reflects that our method reliably detects the DME. For glaucoma detection, we compared our method against the works of Liao et al [70], Chen et al [71], Xu et al [72], Li et al [73], Bajwa et al [37], Ramani et al [39], Parakash et al [74] and Krishna et al [12]. The comparison results using ORIGA, HRF, and DR HAGIS datasets are reported in Table 9.…”

Section: Comparative Studiesmentioning

confidence: 99%

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Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

et al. 2020

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Diabetic patients are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR), diabetic macular edema (DME) and glaucoma. DR is an eye disease that harms the retina and DME is developed by the accumulation of fluid in the macula, while glaucoma damages the optic disk and causes vision loss in advanced stages. However, due to slow progression, the disease shows few signs in early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented. The FRCNN is an object detection approach that requires the bounding-box annotations to work; however, datasets do not provide them, therefore, we have generated these annotations through ground-truths. Afterward, FRCNN is trained over the annotated images for localization that are then segmented-out through FKM clustering. The segmented regions are then compared against the ground-truths through intersection-over-union operations. For performance evaluation, we used the Diaretdb1, MESSIDOR, ORIGA, DR-HAGIS, and HRF datasets. A rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentation.

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Section: Comparative Studiesmentioning

confidence: 51%

Section: Comparative Studiesmentioning

confidence: 99%

Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

et al. 2020

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“…These results are particularly encouraging as any screening program for glaucoma will need to demonstrate a fairly high specificity to minimize the rate of false positive referrals while ensuring that those with functional vision loss are detected. Thus, these and other studies 25,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] have demonstrated that there is a viable potential for medical systems to harness existing teleretinal or other telehealth infrastructure for glaucoma screening if the review of such imaging becomes feasibly automated through DL in the future. Three convolutional layers, one max-pooling layer, and one full connection layer.…”

Section: Deep Learning In Color Fundus Photographsmentioning

confidence: 99%

“…A majority of the studies included in this review utilized CFPs for training and testing the DL algorithm for the diagnosis of glaucoma. 25,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] The details of each study with CFPs is summarized in Table 1. Fundus photography has already been successfully incorporated into teleophthalmology programs to detect other eye diseases, such as diabetic retinopathy.…”

Section: Deep Learning In Color Fundus Photographsmentioning

confidence: 99%

Applications of deep learning in detection of glaucoma: A systematic review

Mirzania

Muir

2020

European Journal of Ophthalmology

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Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.

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“…They used the proposed model to explain the importance of each diagnostic product, medication, and treatment procedure. m) Evidence Activation Mapping (EMANet): Lia et al [123] proposed a CNN based model for glaucoma diagnosis named as EAMNet. The proposed architecture not only able to detect the diseases but also show transparency by highlighting the affected area detected by the system.…”

Section: D) Local Interpretable Model-agnostic Explanations (Lime)mentioning

confidence: 99%

Explainable, Trustworthy, and Ethical Machine Learning for Healthcare: A Survey

Rasheed¹,

Qayyum²,

Ghaly³

et al. 2021

Preprint

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With the advent of machine learning (ML) applications in daily life, the questions about liability, trust, and interpretability of their outputs are raising, especially for healthcare applications. The black-box nature of ML models is a roadblock for clinical utilization. Therefore, to gain the trust of clinicians and patients, researchers need to provide explanations of how and why the model is making a specific decision. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provide a comprehensive review of explainable and interpretable ML techniques implemented for providing the reasons behind their decisions for various healthcare applications. Along with highlighting various security, safety, and robustness challenges that hinder the trustworthiness of ML we also discussed the ethical issues of healthcare ML and describe how explainable and trustworthy ML can resolve these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.

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Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis

Cited by 105 publications

References 26 publications

Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

Applications of deep learning in detection of glaucoma: A systematic review

Explainable, Trustworthy, and Ethical Machine Learning for Healthcare: A Survey

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