Diabetic Retinopathy Related Lesions Detection and Classification Using Machine Learning Technology

Saha, Rituparna; Chowdhury, Anirban; Banerjee, Sreeparna

doi:10.1007/978-3-319-39384-1_65

Cited by 20 publications

(13 citation statements)

References 11 publications

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“…This paper extends our previous works [18][19][20]27] where only DR related abnormalities were detected using Fuzzy c means clustering approach. In this paper, a new way is presented to detect both types of DR lesions (dark and bright) and also dry AMD using multilevel thresholding [24].…”

Section: Introductionsupporting

confidence: 72%

“…These were done after image preprocessing from RGB to green channel image, and noise removal, as required. This was followed by classification using k-nearest neighbor [3], rule based supervised learning [8], support vector machine [12,20] and naïve Bayes machine learning [19] classifiers. In [13], a combination of Gaussian mixture model and k-nearest neighbor classifier has been used to extract the lesions using a reduced number of features.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Retinal Abnormalities Using Machine Learning Methodologies

Saha¹,

Chowdhury²,

Banerjee³

et al. 2018

NNW

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This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naïve Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naïve Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13 % and the classification accuracy of single layer perceptron for bright lesion class is 95.13 % with optimal feature set.

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Section: Introductionsupporting

confidence: 72%

Section: Related Workmentioning

confidence: 99%

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Saha¹,

Chowdhury²,

Banerjee³

et al. 2018

NNW

Self Cite

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“…After all the 44 features extracted from each MA candidate patch, machine learning technique would be used to classify each patch to MA or non-MA. As aforementioned, a variety of classification methods were used in automatic DR detection, including MA detection with Naive Bayesian [16], random forest [17], support vector machine [18,26,39] and K-nearest neighbor [23,24]. Three classifiers were selected for MA detection and compared the results in this work: NB, KNN and SVM.…”

Section: Machine Learning Algorithms For Classificationmentioning

confidence: 99%

“…With the development of computer-aided diagnose (CAD) of DR, due to the importance of MA for DR diagnose, there are more and more studies on automatic detection of MA recently. Saha et al [16] detected red lesions and bright lesions using Naive Bayesian (NB) classifier and support vector machine (SVM), but they only used one database including 100 images for both training and testing. Seoud et al [17] proposed a method based on random forest (RF) for the detection of both MA and HM using dynamic shape features, which did not need any prior segmentation of lesions.…”

mentioning

confidence: 99%

Microaneurysms detection in color fundus images using machine learning based on directional local contrast

Long

Chen

et al. 2020

BioMed Eng OnLine

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Background: As one of the majorcomplications of diabetes, diabetic retinopathy (DR) is a leadingcause of visual impairment and blindness due to delayed diagnosisand intervention. Microaneurysms appear as the earliest symptom ofDR. Accurate and reliable detection of microaneurysms in colorfundus images has great importance for DR screening.Methods: A microaneurysms' detection methodusing machine learning based on directional local contrast (DLC) isproposed for the early diagnosis of DR. First, blood vessels wereenhanced and segmented using improved enhancement function based onanalyzing eigenvalues of Hessian matrix. Next, with blood vesselsexcluded, microaneurysm candidate regions were obtained using shapecharacteristics and connected components analysis. After imagesegmented to patches, the features of each microaneurysm candidatepatch were extracted, and each candidate patch was classified intomicroaneurysm or non-microaneurysm. The main contributions of ourstudy are (1) making use of directional local contrast inmicroaneurysms' detection for the first time, which does make sensefor better microaneurysms' classification.(2) Applying threedifferent machine learning techniques for classification andcomparing their performance for microaneurysms' detection. Theproposed algorithm was trained and tested on e-ophtha MA database,and further tested on another independent DIARETDB1 database.Results of microaneurysms' detection on the two databases wereevaluated on lesion level and compared with existing algorithms. Results:The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. Conclusions:The proposed methodusing machine learning based on directional local contrast of imagepatches can effectively detect microaneurysms in color fundus imagesand provide an effective scientific basis for early clinical DRdiagnosis.

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“…CWS is caused when retinal nerve fiber layers are damaged because it includes confined aggregations of axoplasmic debris inside contiguous groups of unmyelinated ganglion cell axons (McLeod, 2005). CWS (Shabbir, Sharif, Nisar, Yasmin, & Fernandes, 2016) are usually found in the patients who are suffering from the disease called hypertension and diabetes mellitus (Saha, Chowdhury, & Banerjee, 2016). The CWS is normally based on retinal lesions as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Diabetic retinopathy detection and classification using hybrid feature set

Amin

Sharif

Rehman

et al. 2018

Microscopy Res & Technique

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Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E‐ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).

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Diabetic Retinopathy Related Lesions Detection and Classification Using Machine Learning Technology

Cited by 20 publications

References 11 publications

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Microaneurysms detection in color fundus images using machine learning based on directional local contrast

Diabetic retinopathy detection and classification using hybrid feature set

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