Detection of non-proliferative diabetic retinopathy lesions using wavelet and classification using K-means clustering

Rajput, Yogesh M.; Manza, Ramesh R.; Patwari, Manjiri B.; Rathod, Deepali D.; Borde, Prashant L.; Yannawar, Pravin

doi:10.1109/iccn.2015.73

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Biologically, the ophthalmic artery is an integral subsidiary of the internal carotid artery and the leading vascular provider for the retina (9); variation of the hemodynamics of the internal carotid artery definitely may result in anomalies of the retinal microvasculature (9). Fundus images can be an indirect barometer of systemic disease (10)(11)(12). More notably, artificial intelligence (AI) predicting methods from retinal images, which have significant advantages in multifactorial issues with high-dimensional data, has been widely applied in systemic disease diagnostics, such as cardiovascular diseases (13), cerebrovascular accidents (14), chronic renal disorders Alzheimer's disease (15), and carotid artery stenosis (16).…”

Section: Introductionmentioning

confidence: 99%

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images

Gong,

Fu,

et al. 2024

Front. Endocrinol.

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ObjectiveTo develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM).MethodsIn total, 1236 patients with T2DM who had both retinal fundus images and CIMT ultrasound records within a single hospital stay were enrolled. Data were divided into normal and thickened groups and sent to eight deep learning models: convolutional neural networks of the eight models were all based on ResNet or ResNeXt. Their encoder and decoder modes are different, including the standard mode, the Parallel learning mode, and the Siamese mode. Except for the six unimodal networks, two multimodal networks based on ResNeXt under the Parallel learning mode or the Siamese mode were embedded with ages. Performance of eight models were compared via the confusion matrix, precision, recall, specificity, F1 value, and ROC curve, and recall was regarded as the main indicator. Besides, Grad-CAM was used to visualize the decisions made by Siamese ResNeXt network, which is the best performance.ResultsPerformance of various models demonstrated the following points: 1) the RexNeXt showed a notable improvement over the ResNet; 2) the structural Siamese networks, which extracted features parallelly and independently, exhibited slight performance enhancements compared to the traditional networks. Notably, the Siamese networks resulted in significant improvements; 3) the performance of classification declined if the age factor was embedded in the network. Taken together, the Siamese ResNeXt unimodal model performed best for its superior efficacy and robustness. This model achieved a recall rate of 88.0% and an AUC value of 90.88% in the validation subset. Additionally, heatmaps calculated by the Grad-CAM algorithm presented concentrated and orderly mappings around the optic disc vascular area in normal CIMT groups and dispersed, irregular patterns in thickened CIMT groups.ConclusionWe provided a Siamese ResNeXt neural network for predicting the carotid intimal thickness of patients with T2DM from fundus images and confirmed the correlation between fundus microvascular lesions and CIMT.

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Section: Introductionmentioning

confidence: 99%

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images

Gong,

Fu,

et al. 2024

Front. Endocrinol.

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“…The optic disc is a bright yellow approximately circular object on the retina where major blood vessels appear, providing a reference location for other locations in the fundus image 7 , as shown in Figure 1. Since there are various characteristic lesions caused by diabetic retinopathy at different stages 8 , the priority of segmenting the obvious optic disc on the retina can improve the accuracy of detecting other serious lesions, and is more conducive to the diagnosis of doctors.…”

Section: Introductionmentioning

confidence: 99%

Optic disc segmentation in retinal fundus images using improved CE-Net

wang

Huang²

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Diabetic retinopathy is one of the main complications of diabetes and the most important factor leading to blindness in the late stage of the disease. It often manifests as one or more lesions in clinical diagnosis. In order to reduce the difficulty of detection, it is of great significance to segment the optic disc in retinal images. This paper proposes an improved context encoding network architecture (CE-Net) for segmentation of the optic disc portion in diabetic retinal images. The network architecture is divided into three parts: feature encoder module, context extractor module and feature decoder module. The context extractor module consists of an improved dense atrous convolutional block (DAC) and residual multi-kernel pooling (RMP). Experimental result shows that the optimal network model generated by the improved CE-Net architecture has good performance on the Indian Diabetic Retinopathy Image Dataset (IDRID), and compared with other methods, our method has the lowest mean overlap error and the highest accuracy and sensitivity.

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“…Rajput et al . developed a method to detect CWSs based on multiresolution analysis using symlet wavelet to extract the lesions of DR. Then they applied K‐means clustering to classify the CWSs; 92% accuracy was achieved. However, they did not describe the OD elimination and feature extraction methods clearly.…”

Section: Introductionmentioning

confidence: 99%

Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine

Sreng

Maneerat

Hamamoto

et al. 2019

IEEJ Transactions Elec Engng

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Diabetic retinopathy is the major issue of diabetes-induced blindness worldwide but is curable if detected in time. Cotton wool spots (CWSs) are the critical lesions of diabetic retinopathy, which indicate not only advanced nonproliferative but also preproliferative diabetic retinopathy. It is crucial to detect CWSs for grading the severity of diabetic retinopathy. By grading the severity of diabetic retinopathy accurately, the eye specialist can make an effective treatment plan to protect the patient's vision against blindness. CWSs detection remains challenging because of their uneven appearance, in which some CWSs are not clearly visible and some resemble hard exudates. This paper proposed an automatic CWS detection method based on adaptive thresholding and ant colony optimization (ACO) coupled with support vector machine (SVM). One-hundred and sixtytwo features from five feature sets, namely morphologies, first-order statistics, gray-level co-occurrence matrix, gray-level run length matrix, and lacunarity, are extracted, and then four feature selection methods,namely genetic algorithm, particle swarm optimization, stepwise method, and ACO, are coupled with SVM classifiers. The evaluation results of the proposed methods on local, standard diabetic retinopathy database calibration level 1, and high-resolution fundus image database datasets containing 319 images indicate that ACO coupling cubic SVM performs better than the other pairs with sensitivity 90.16%, specificity 97.92%, accuracy 96.96%, and area under receiver operating characteristic curve 97.19%. , since 2009. He is currently a Dean of the School of Information and Telecommunication Engineering, Tokai University. His current research interests include information design. Especially, he is interested in medical information, human interface design, and virtual reality. He has about 60 papers published in Transactions and Journals and about 85 papers presented at international conferences. Prof. Hamamoto is a member of the IEEE and many national societies in Japan. He also takes active part in national societies as a committee member. RonakornPanjaphongse (Non-member) received the M.D. degree from the Faculty of Medicine Siriraj Hospital, Bangkok, Thailand, in 2001. He is currently the Head of the Retinal Unit, Ophthalmology Department, Bhumibol Adulyadej Hospital, Royal Thai Air Force, Bangkok, Thailand. His interests include studies related to retinal diseases, especially in diabetic retinopathy, including applying artificial intelligence for medical imaging.

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Detection of non-proliferative diabetic retinopathy lesions using wavelet and classification using K-means clustering

Cited by 13 publications

References 18 publications

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images

Optic disc segmentation in retinal fundus images using improved CE-Net

Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine

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