Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy

Gonçalves, João; Conceição, Teresa; Soares, Filipe

doi:10.5220/0007580904810491

Cited by 7 publications

(6 citation statements)

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“…The 2019 CNN model with Inception-V3 in EyePACS data is an automated diagnostic system based on Deep Learning trained in EyePACS and Messidor datasets that automatically identifies DR in digital colour fundus images [13]. It provides a dichotomised classification as presence or absence of DR or referable DR.…”

Section: Index Testmentioning

confidence: 99%

“…Current research includes the estimation of diagnostic accuracy of deep learning-based diagnostic systems [12]. The Convolutional Neural Network (CNN) model with Inception-V3 is an artificial neural network based on deep learning for automated detection of DR [13]. In a previous pilot study to determine preliminary safety and performance with individuals with diabetes referred to ophthalmology because of DR, the software achieved a sensitivity of 74% and a specificity of 95% [14].…”

Section: Introductionmentioning

confidence: 99%

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Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

et al. 2020

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Purpose: To evaluate the diagnostic accuracy of a diagnostic system software for the automated screening of diabetic retinopathy (DR) on digital colour fundus photographs, the 2019 Convolutional Neural Network (CNN) model with Inception-V3. Methods: In this cross-sectional study 295 fundus images were analysed by the CNN model and compared to a panel of ophthalmologists. Images were obtained from a dataset acquired within a screening programme. Diagnostic accuracy measures and respective 95% confidence intervals (CI) were calculated. Results: The sensitivity and specificity of the CNN model in diagnosing referable DR was 81% [95% confidence interval (CI), 66%−90%] and 97% (95% CI, 95%−99%), respectively. Positive predictive value was 86% (95% CI, 72%−94%) and negative predictive value 96% (95% CI, 93%−98%). The positive likelihood ratio was 33 (95% CI, 15−75) and the negative was 0.20 (95% CI, 0.11−0.35). Its clinical impact is demonstrated by the change observed in the pre-test probability of referable DR (assuming a prevalence of 16%) to a post-test probability for a positive test result of 86% and for a negative test result of 4%. Conclusion: A CNN model negative test result safely excludes DR and its use may significantly reduce the burden of ophthalmologists at reading centres.

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Section: Index Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

et al. 2020

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“…A comparison had been undertaken between traditional approaches and CNN-based approaches. The Inception v3 model has been nonpareil, having reached the accuracy of 89% on the EyePACS dataset and performing the best [ 13 ]. In another study, the fundus images were classified into average to extreme conditions versus non-proliferative DR [ 14 ], where they used backpropagation neural organization (BPNN).…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Diabetic Retinopathy Using Deep Learning: On the Search of Segmented Retinal Blood Vessel Images for Better Performance

et al. 2023

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Diabetic retinopathy is one of the most significant retinal diseases that can lead to blindness. As a result, it is critical to receive a prompt diagnosis of the disease. Manual screening can result in misdiagnosis due to human error and limited human capability. In such cases, using a deep learning-based automated diagnosis of the disease could aid in early detection and treatment. In deep learning-based analysis, the original and segmented blood vessels are typically used for diagnosis. However, it is still unclear which approach is superior. In this study, a comparison of two deep learning approaches (Inception v3 and DenseNet-121) was performed on two different datasets of colored images and segmented images. The study’s findings revealed that the accuracy for original images on both Inception v3 and DenseNet-121 equaled 0.8 or higher, whereas the segmented retinal blood vessels under both approaches provided an accuracy of just greater than 0.6, demonstrating that the segmented vessels do not add much utility to the deep learning-based analysis. The study’s findings show that the original-colored images are more significant in diagnosing retinopathy than the extracted retinal blood vessels.

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“…DR identification performance was improved by the authors in [14] using six different CNN architectures using the deep learning concept. A study was presented by the authors in [15] that compares the traditional DR classification methods to the deep learning-based approaches using various standard datasets. Authors in [16] developed a cross-disease review network for finding various specific characteristics of intrinsic relationship between diabetic macular edema (DME) and DR. Bhardwaj, et al [17] presented a Quadrant based Inception-V3 network for DR grading using the two benchmark datasets (MESSIDOR and IDRiD).…”

Section: Introductionmentioning

confidence: 99%

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Zhang

Luo²,

Cengiz

2021

IJCAI

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The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.Povzetek: Opisana je metoda ugotavljanja sladkorne bolezni iz slik očesnega ozadje na osnovi strojnega in globokega učenja.

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Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy

Cited by 7 publications

References 0 publications

Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

Automated Diagnosis of Diabetic Retinopathy Using Deep Learning: On the Search of Segmented Retinal Blood Vessel Images for Better Performance

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

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