ArticleSensors (Basel, Switzerland)2021
Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 75 citations in OpenAlex.
- Predicting progression to proliferative diabetic retinopathy using automated versus manual quantification of retinal haemorrhages.Eye (London, England) · 2026Article
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- The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features.Journal of imaging informatics in medicine · 2025Article
- Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and ChiScientific reports · 2025Article
- CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
- Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.PloS one · 2024Article
- Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion.Foods (Basel, Switzerland) · 2023Article
- Drusen-aware model for age-related macular degeneration recognition.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2023Article
- Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement.Healthcare (Basel, Switzerland) · 2023Article
- Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review.Multimedia tools and applications · 2023Article
- Hemorrhage segmentation in mobile-phone retinal images using multiregion contrast enhancement and iterative NICK thresholding region growing.Scientific reports · 2022Article
- Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.Medicina (Kaunas, Lithuania) · 2022Article
- Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification.Translational vision science & technology · 2022Article
- Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
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Abstract
Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we propose a new method for accurate hemorrhage detection from the retinal fundus images. First, the proposed method uses the modified contrast enhancement method to improve the edge details from the input retinal fundus images. In the second stage, a new convolutional neural network (CNN) architecture is proposed to detect hemorrhages. A modified pre-trained CNN model is used to extract features from the detected hemorrhages. In the third stage, all extracted feature vectors are fused using the convolutional sparse image decomposition method, and finally, the best features are selected by using the multi-logistic regression controlled entropy variance approach. The proposed method is evaluated on 1509 images from HRF, DRIVE, STARE, MESSIDOR, DIARETDB0, and DIARETDB1 databases and achieves the average accuracy of 97.71%, which is superior to the previous works. Moreover, the proposed hemorrhage detection system attains better performance, in terms of visual quality and quantitative analysis with high accuracy, in comparison with the state-of-the-art methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.