Abstract
References
- Shome SK, Vadali SRK. Enhancement of diabetic retinopathy imagery using contrast limited adaptive histogram equalization, Int J Computer Sci Inform Technol 2011; 2: 2694-9.
- Mohamed Q, Gillies MC, Wong TY. Management of diabetic retinopathy: a systematic review, Jama, 2007; 298: 902-16.
- Solomon SD, Goldberg MF. ETDRS grading of diabetic retinopathy: still the gold standard? Ophthalmic Research 2019; 62: 190-5.
- Wu L, Fernandez-Loaiza P, Sauma J, Hernandez-Bogantes E, Masis M. Classification of diabetic retinopathy and diabetic macular edema, World J Diabetes 2013; 4: 290-4.
- Flaxel CJ, Adelman RA, Bailey ST, et al. Diabetic retinopathy preferred practice pattern®, Ophthalmology 2020; 127: 66-145.
- Ogurtsova K, Fernandes JdaR, Huang Y, et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040, Diabetes Research Clin Practice 2017; 128: 40-50.
- Lian F, Wu L, Tian J, et al. The effectiveness and safety of a danshen-containing Chinese herbal medicine for diabetic retinopathy: a randomized, double-blind, placebo-controlled multicenter clinical trial. J Ethnopharmacol 2015; 164 : 71-7.
- Tan F, Chen Q, Zhuang X, et al. Associated risk factors in the early stage of diabetic retinopathy, Eye and Vision 2019; 6: 1-10.
- Cole ED, Novais EA, Louzada RN, Waheed NK. Contemporary retinal imaging techniques in diabetic retinopathy: a review, Clin Experime Ophthalmol 2016; 44: 289-99.
- Kwan CC, Fawzi AA. Imaging and biomarkers in diabetic macular edema and diabetic retinopathy, Current Diabetes Reports 2019; 19: 1-10.
- Gao Z, Jin K, Yan Y, et al. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning, Graefe's Archive Clin Experime Ophthalmol 2022; 260: 1663-73.
- Stratton IM, Adler AI, Neil HAW, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000; 321: 405-12.
- Sacks FM, Hermans MP, Fioretto P, et al. Association between plasma triglycerides and high-density lipoprotein cholesterol and microvascular kidney disease and retinopathy in type 2 diabetes mellitus: a global case–control study in 13 countries. Circulation 2014; 129: 999-1008.
- Morton J, Zoungas S, Li Q, et al. Low HDL cholesterol and the risk of diabetic nephropathy and retinopathy: results of the ADVANCE study, Diabetes Care 2012; 35: 2201-6.
- El Rami H, Barham R, Sun JK, Silva PS. Evidence-based treatment of diabetic retinopathy, in: Seminars in Ophthalmology, Taylor & Francis, 2017, pp. 67-74.
- Akkaya S, Acikalin B, Asilyazici E, Yilmaz A, Yamic M, Kocapinar Y. Diagnosis and treatment of diabetic retinopathy. Retina-Vitreus/Journal of Retina-Vitreous 2018; 27: 390-401
- Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review, BMC Medical Informatics and Decision Making, 2021; 21: 1-23.
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nature Reviews Cancer 2018; 18: 500-10.
- Hipwell J, Strachan F, Olson J, McHardy K, Sharp P, Forrester J. Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool, Diabetic Medicine 2000; 17: 588-94.
- Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: a natural step to the future, Indian J Ophthalmol 2019; 67: 1004-9.
- Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L.-C. Mobilenetv2: inverted residuals and linear bottlenecks, in: Proceedings of the IEEE conference on computer vision and pattern recognition. CVPR 2018; 4510-20.
- Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR. Neighbourhood components analysis. Advances in Neural Information Processing Systems 2004; 17: 513-20.
- Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Communications of the ACM 2017; 60: 84-90.
- Peterson LE. K-nearest neighbor. Scholarpedia 2009; 4: 1883.
- Warrens MJ. On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index. J Classification 2008; 25: 177-83.
- Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv 2020: 2010.16061.
An automated diabetic retinopathy disorders detection model based on pretrained MobileNetv2 and nested patch division using fundus images
Abstract
Aim: Fundus images are very important to diagnose some ophthalmologic disorders. Hence, fundus images have become a very important data source for machine-learning society. Our primary goal is to propose a new automated disorder classification model for diabetic retinopathy (DR) using the strength of deep learning. In this model, our proposed model suggests a treatment technique using fundus images.
Material and Method: In this research, a new dataset was acquired and this dataset contains 1365 Fundus Fluorescein Angiography images with five classes. To detect these disorders automatically, we proposed a transfer learning-based feature engineering model. This feature engineering model uses pretrained MobileNetv2 and nested patch division to extract deep and exemplar features. The neighborhood component analysis (NCA) feature selection function has been applied to choose the top features. k nearest neighbors (kNN) classification function has been used to get results and we used 10-fold cross-validation (CV) to validate the results.
Results: The proposed MobileNetv2 and nested patch-based image classification model attained 87.40% classification accuracy on the collected dataset.
Conclusions: The calculated 87.40% classification accuracy for five classes has been demonstrated high classification accuracy of the proposed deep feature engineering model
Keywords
Diabetic retinopathy fundus image processing biomedical image classification artificial intelligence
References
- Shome SK, Vadali SRK. Enhancement of diabetic retinopathy imagery using contrast limited adaptive histogram equalization, Int J Computer Sci Inform Technol 2011; 2: 2694-9.
- Mohamed Q, Gillies MC, Wong TY. Management of diabetic retinopathy: a systematic review, Jama, 2007; 298: 902-16.
- Solomon SD, Goldberg MF. ETDRS grading of diabetic retinopathy: still the gold standard? Ophthalmic Research 2019; 62: 190-5.
- Wu L, Fernandez-Loaiza P, Sauma J, Hernandez-Bogantes E, Masis M. Classification of diabetic retinopathy and diabetic macular edema, World J Diabetes 2013; 4: 290-4.
- Flaxel CJ, Adelman RA, Bailey ST, et al. Diabetic retinopathy preferred practice pattern®, Ophthalmology 2020; 127: 66-145.
- Ogurtsova K, Fernandes JdaR, Huang Y, et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040, Diabetes Research Clin Practice 2017; 128: 40-50.
- Lian F, Wu L, Tian J, et al. The effectiveness and safety of a danshen-containing Chinese herbal medicine for diabetic retinopathy: a randomized, double-blind, placebo-controlled multicenter clinical trial. J Ethnopharmacol 2015; 164 : 71-7.
- Tan F, Chen Q, Zhuang X, et al. Associated risk factors in the early stage of diabetic retinopathy, Eye and Vision 2019; 6: 1-10.
- Cole ED, Novais EA, Louzada RN, Waheed NK. Contemporary retinal imaging techniques in diabetic retinopathy: a review, Clin Experime Ophthalmol 2016; 44: 289-99.
- Kwan CC, Fawzi AA. Imaging and biomarkers in diabetic macular edema and diabetic retinopathy, Current Diabetes Reports 2019; 19: 1-10.
- Gao Z, Jin K, Yan Y, et al. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning, Graefe's Archive Clin Experime Ophthalmol 2022; 260: 1663-73.
- Stratton IM, Adler AI, Neil HAW, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000; 321: 405-12.
- Sacks FM, Hermans MP, Fioretto P, et al. Association between plasma triglycerides and high-density lipoprotein cholesterol and microvascular kidney disease and retinopathy in type 2 diabetes mellitus: a global case–control study in 13 countries. Circulation 2014; 129: 999-1008.
- Morton J, Zoungas S, Li Q, et al. Low HDL cholesterol and the risk of diabetic nephropathy and retinopathy: results of the ADVANCE study, Diabetes Care 2012; 35: 2201-6.
- El Rami H, Barham R, Sun JK, Silva PS. Evidence-based treatment of diabetic retinopathy, in: Seminars in Ophthalmology, Taylor & Francis, 2017, pp. 67-74.
- Akkaya S, Acikalin B, Asilyazici E, Yilmaz A, Yamic M, Kocapinar Y. Diagnosis and treatment of diabetic retinopathy. Retina-Vitreus/Journal of Retina-Vitreous 2018; 27: 390-401
- Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review, BMC Medical Informatics and Decision Making, 2021; 21: 1-23.
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nature Reviews Cancer 2018; 18: 500-10.
- Hipwell J, Strachan F, Olson J, McHardy K, Sharp P, Forrester J. Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool, Diabetic Medicine 2000; 17: 588-94.
- Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: a natural step to the future, Indian J Ophthalmol 2019; 67: 1004-9.
- Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L.-C. Mobilenetv2: inverted residuals and linear bottlenecks, in: Proceedings of the IEEE conference on computer vision and pattern recognition. CVPR 2018; 4510-20.
- Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR. Neighbourhood components analysis. Advances in Neural Information Processing Systems 2004; 17: 513-20.
- Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Communications of the ACM 2017; 60: 84-90.
- Peterson LE. K-nearest neighbor. Scholarpedia 2009; 4: 1883.
- Warrens MJ. On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index. J Classification 2008; 25: 177-83.
- Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv 2020: 2010.16061.
Details
| Primary Language | English |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Original Article |
| Authors | |
| Publication Date | October 25, 2022 |
| Published in Issue | Year 2022 Volume: 5 Issue: 6 |
Cite
Cited By
Pretrained Models and the Role of Feature Selection: An Artificial Intelligence-Based Approach in the Diagnosis of Diabetic Retinopathy
Turkish Journal of Science and Technology
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