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

Long, Shengchun; Chen, Jiali; Hu, Ante; Liu, Haipeng; Chen, Zhiqing; Zheng, Dingchang

doi:10.1186/s12938-020-00766-3

Cited by 44 publications

(28 citation statements)

References 41 publications

(51 reference statements)

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“…In this study, MATLAB (version 2018A) is utilized for simulations in Personal Computer consists of 8 GB RAM, 3.0 GHz Intel i5 processor and one TB hard disc. The performance of the proposed model is compared with some previous research models; FCNN [1], Local Convergence Index (LCI) features [13], local features with K-Nearest Neighbour (KNN) Classifier [14] and DLC [21] on the E-ophtha and DiaretDB1 databases in terms of sensitivity against FPI, AUC, f-score, and accuracy to estimate efficacy of the proposed model. The performance measure is determined as the procedure of investigating, collecting and reporting the information based on system performance.…”

Section: Resultsmentioning

confidence: 99%

“…The Table 2 presents the performance valuation of one sample image in DiaretDB1 dataset. For the collected fundus image, f-score of the proposed technique is 0.610 and the existing works; FCNN [1], LCI features [13] and DLC [21] delivers 0.392, 0.547 and 0.210 of f-score. In addition, the AUC of the proposed technique is 0.725 and the existing LCI features [13] delivers 0.565 of AUC.…”

Section: Quantitative Analysis On Diaretdb1 Datasetmentioning

confidence: 87%

“…The performance evaluation is validated for one sample fundus image in the E-ophtha database. For the collected image, f-score of the proposed technique is 0.5781 and the existing models LCI features [13], local features with KNN Classifier [14], and DLC [21] delivers 0.51, 0.273 and 0.374 of f-score. In addition, AUC of the proposed technique is 0.6796 and the existing methodologies delivers 0.575 and 0.386 of AUC.…”

Section: Quantitative Analysis On E-ophtha Datasetmentioning

confidence: 97%

“…S. Long, J. Chen, A. Hu, H. Liu, Z. Chen and D. Zheng [21] used Directional Local Contrast (DLC) algorithm for detecting the microaneurysms. Initially, the blood vessel components were enhanced and segmented by Eigen values of Hessian matrix.…”

Section: Literature Surveymentioning

confidence: 99%

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Detection of Microaneurysms Using Grey Wolf Optimization for Early Diagnosis of Diabetic Retinopathy

Pundikal¹,

Holi²

2020

IJIES

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The diabetic retinopathy is the leading cause of blindness worldwide, so early detection of diabetic retinopathy is necessary to reduce eye-related diseases. The accurate identification of microaneurysms is crucial for the detection of diabetic retinopathy, because it appears as the first sign of the disease. In this study, a new model is proposed to detect microaneurysms from the retinal images for early diagnosis of diabetic retinopathy. At first, the fundus images are collected from e-ophtha microaneurysms and DiaretDB1 datasets. Next, image pre-processing is accomplished using image normalization, low light image enhancement, gradient weighting and shade correction. The pre-processing methods significantly brighten the contrast of the fundus images for better visual quality and extract the hidden details of the dark conditions. In addition, Hessian based filter and Otsu threshold are used to extract the foreground objects like microaneurysms from the enhanced fundus images. At last, Grey Wolf Optimization (GWO) is used to predict the correctness of segmented microaneurysms candidates. The experimental results have revealed that the proposed model enhanced the microaneurysms detection up to 0.06-0.30 f-score value compared to the other existing models local convergence index features and local features with k-nearest neighbor. In addition, the proposed model has achieved 85.72% and 86.16% of accuracy respectively on e-ophtha microaneurysms and DiaretDB1 datasets.

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Section: Resultsmentioning

confidence: 99%

Section: Quantitative Analysis On Diaretdb1 Datasetmentioning

confidence: 87%

Section: Quantitative Analysis On E-ophtha Datasetmentioning

confidence: 97%

Section: Literature Surveymentioning

confidence: 99%

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Detection of Microaneurysms Using Grey Wolf Optimization for Early Diagnosis of Diabetic Retinopathy

Pundikal¹,

Holi²

2020

IJIES

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“…Then, the images were segmented using H-maxima and thresholding technique. Long et al [3] proposed a microaneurysms' detection method using machine learning based on directional local contrast (DLC) for the early diagnosis of DR. Sarhan et al [4] proposed a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. Yang et al [5] proposed a method based on improved Hessian matrix eigenvalue analysis to detect microaneurysms and hemorrhages in the fundus images of diabetic patients.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Wang¹,

Yao²,

Li³

et al. 2021

Preprint

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Diabetic retinopathy (DR) is a common eye disease, which leads cause of blindness all around the world. Microaneurysms (MAs) is one of the early symptoms of DR. Accurate and effective MAs detection and segmentation is an important step for the diagnosis and treatment of DR. In this paper, we propose an automatic model for detection of MAs in fluorescein fundus angiography (FFA) images. The model mainly consists of two steps. The first step is pre-processing of FFA images, where the quality of FFA images is improved by Histogram Stretching and Gaussian Filtering algorithm. The second step is to detect MAs regions, where the MAs regions are detected by improved FC-DenseNet. We compare the proposed model with traditional FC-DenseNet model and other previously published models. The experimental result shows that our proposed model has the highest scores on evaluation metrics of pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1) and mean intersection over union (MIoU), which are 99.97%, 94.19%, 88.40%, 89.70%, 88.98% and 90.14%, respectively. The result suggests that the performance of our proposed model is closer to the ground truth of MAs detection. Our proposed model would be helpful for ophthalmologists to find the symptoms more quickly and to take better treatment measures in the screening process of diabetic retinopathy.

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Micro aneurysm detection using optimized residual‐based temporal attention Convolutional Neural Network with Inception‐V3 transfer learning

Alotaibi

2024

Microscopy Res & Technique

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In this manuscript micro aneurysm detection using residual‐based temporal attention Convolutional Neural Network (CNN) with Inception‐V3 transfer learning optimized with equilibrium optimization algorithm (MA‐RTCNN‐Inception V3‐EOA) is proposed. The proposed research work contains four phases: (1) pre‐processing, (2) segmentation, (3) post‐processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA‐RTCNN‐Inception V3‐EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f‐measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non‐proliferative diabetic retinopathy depending on deep learning approaches (DRD‐CNN‐NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN‐AMD‐MAFP‐Net) respectively.Research Highlights Micro aneurysm detection using residual‐based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT‐CNN algorithm successfully identifies the micro aneurysms using EOA.

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

Cited by 44 publications

References 41 publications

Detection of Microaneurysms Using Grey Wolf Optimization for Early Diagnosis of Diabetic Retinopathy

Detection of Microaneurysms Using Grey Wolf Optimization for Early Diagnosis of Diabetic Retinopathy

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Micro aneurysm detection using optimized residual‐based temporal attention Convolutional Neural Network with Inception‐V3 transfer learning

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