Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

Zou, Xiaochun; Zhao, Xinbo; Yang, Yongjia; Li, Na

doi:10.1155/2016/7496735

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…The technique [ 10 ] outperforms the challenging database; however, it may not perform well over the samples with post-processing operations. Zou et al [ 11 ] introduced a method for DME detection from fundus images. Both feature and position properties are combined by employing SVM and the Bayesian probability theory to locate the DME from the input samples.…”

Section: Related Workmentioning

confidence: 99%

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Nazir

Nawaz

Rashid

et al. 2021

Sensors

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Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

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Section: Related Workmentioning

confidence: 99%

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Nazir

Nawaz

Rashid

et al. 2021

Sensors

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“…Computational saliency models have been reported for only HE. 31,32 Our aim is to develop saliency models for both HE and HM. This is done using a CNN inspired by the Itti-Koch saliency model.…”

Section: Saliency Computation 21 Backgroundmentioning

confidence: 99%

Assistive lesion-emphasis system: an assistive system for fundus image readers

Rangrej

Sivaswamy

2017

J. Med. Imag

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Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader's attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues. We present an assistive lesion-emphasis system (ALES) based on the above idea, for fundus image-based diabetic retinopathy diagnosis. Lesion-saliency is learnt using a convolutional neural network (CNN), inspired by the saliency model of Itti and Koch. The CNN is used to fine-tune standard low-level filters and learn high-level filters for deriving a lesion-saliency map, which is then used to perform lesion-emphasis via a spatially variant version of gamma correction. The proposed system has been evaluated on public datasets and benchmarked against other saliency models. It was found to outperform other saliency models by 6% to 30% and boost the contrast-to-noise ratio of lesions by more than 30%. Results of a perceptual study also underscore the effectiveness and, hence, the potential of ALES as an assistive tool for readers.

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“…Saliency prediction, wherein the objective is to automatically predict what most attracts human attention in view-free scenarios, is a long-standing classical research topic concerning visual-cognition, computer sciences [ 1 , 2 ], and imaging techniques [ 3 – 6 ]. Saliency models can be broadly classified into two types—(1) salient-object detection and (2) saliency prediction.…”

Section: Introductionmentioning

confidence: 99%

“…In the last two decades, numerous saliency prediction methods for RGB images have been significantly improved, and various models have been proposed [ 17 – 19 ]. However, several extant studies [ 1 , 8 , 11 ] reveal that features extracted from two modalities—depth maps and RGB images—complement each other. RGB images contain discriminative visual-appearance information, whereas depth maps include geometric features concerning objects.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency

Zhou

2020

Computational Intelligence and Neuroscience

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Visual saliency prediction for RGB-D images is more challenging than that for their RGB counterparts. Additionally, very few investigations have been undertaken concerning RGB-D-saliency prediction. The proposed study presents a method based on a hierarchical multimodal adaptive fusion (HMAF) network to facilitate end-to-end prediction of RGB-D saliency. In the proposed method, hierarchical (multilevel) multimodal features are first extracted from an RGB image and depth map using a VGG-16-based two-stream network. Subsequently, the most significant hierarchical features of the said RGB image and depth map are predicted using three two-input attention modules. Furthermore, adaptive fusion of saliencies concerning the above-mentioned fused saliency features of different levels (hierarchical fusion saliency features) can be accomplished using a three-input attention module to facilitate high-accuracy RGB-D visual saliency prediction. Comparisons based on the application of the proposed HMAF-based approach against those of other state-of-the-art techniques on two challenging RGB-D datasets demonstrate that the proposed method outperforms other competing approaches consistently by a considerable margin.

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Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

Cited by 19 publications

References 25 publications

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Assistive lesion-emphasis system: an assistive system for fundus image readers

Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency

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