Microaneurysm detection using color locus detection method

Yadav, Divakar; Karn, Arun Kumar; Giddalur, Anurag; Dhiman, Arti; Sharma, Sakshi; Muskan,; Yadav, Arun Kr

doi:10.1016/j.measurement.2021.109084

Cited by 21 publications

(14 citation statements)

References 23 publications

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“…The performance of the proposed scheme was compared with the traditional schemes such as GVA [ 7 ], Sparse PCA [ 37 ], LCI features [ 9 ], Two-step CNN [ 10 ], DLC [ 21 ] and CLD [ 31 ]. The F1 Score of the proposed scheme is 90.47 % which is less than the scheme CLD.…”

Section: Resultsmentioning

confidence: 99%

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Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

Latha¹,

Bell²,

Sheela³

2022

Multimed Tools Appl

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Red lesion identification at its early stage is very essential for the treatment of diabetic retinopathy to prevent loss of vision. This work proposes a red lesion detection algorithm that uses Hexagonal pattern-based features with two-level segmentation that can detect hemorrhage and microaneurysms in the fundus image. The proposed scheme initially pre-processes the fundus image followed by a two-level segmentation. The level 1 segmentation eliminates the background whereas the level 2 segmentation eliminates the blood vessels that introduce more false positives. A hexagonal pattern-based feature is extracted from the red lesion candidates which can highly differentiate the lesion from non-lesion regions. The hexagonal pattern features are then trained using the recurrent neural network and are classified to eliminate the false negatives. For the evaluation of the proposed red lesion algorithm, the datasets namely ROC challenge, e-ophtha, DiaretDB1, and Messidor are used with the metrics such as Accuracy, Recall, Precision, F1 score, Specificity, and AUC. The scheme provides an average Accuracy, Recall (Sensitivity), Precision, F1 score, Specificity, and AUC of 95.48 % , 84.54 % , 97.3 % , 90.47 % , 86.81 % and 93.43 % respectively.

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Section: Resultsmentioning

confidence: 99%

“…The average AUC (area under the curve) of this approach is 86.5 % . The authors Yadev et al [ 31 ] used a color locus detection method for detecting the MAs. It uses the basis of the histogram for segmenting the retinal images.…”

Section: Related Workmentioning

confidence: 99%

Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

Latha¹,

Bell²,

Sheela³

2022

Multimed Tools Appl

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“…Furthermore, researchers [13] used hybrid color, texture features, and customized CNNs (MCNN) for a multi-class lesion classification system. Moreover, researchers have used color to detect lesions [14]. In [14] researchers applied machine learning algorithms to identify microaneurysms (MAs) in fundus images using e-ophtha dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, researchers have used color to detect lesions [14]. In [14] researchers applied machine learning algorithms to identify microaneurysms (MAs) in fundus images using e-ophtha dataset. Among all ML classifiers, they used decision trees, Support Vector Machine (SVM) and Logistic Regression (LR), k-nearest neighbors (k-NN), Random Forest (RF), and Naive Bayes (NB) classifiers to distinguish MAs from healthy fundus images.…”

Section: Related Workmentioning

confidence: 99%

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DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Shenavarmasouleh,

Mohammadi,

Amini

et al. 2021

Preprint

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Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP). We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.

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