Relevant bibliographies by topics / Microaneurysms (MA)

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Academic literature on the topic 'Microaneurysms (MA)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Microaneurysms (MA)"

1

Dafwen Toresa, Nor Hazlyna Harun, Juhaida Abu Bakar. "Detection of Microaneurysm Model Using Kmean Clustering Method, Hough Transform Optimization and CNN." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 551–62. https://doi.org/10.52783/jisem.v10i1s.6845.

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Introduction: Diabetic retinopathy (DR) is a chronic disease that damages the retina due to damage to the small blood vessels caused by diabetes mellitus. This disease is one of the main causes of visual impairment in people with diabetes. Early detection of clinical signs of DR is essential to allow for effective intervention and treatment. Ophthalmologists are trained to recognize DR by examining small changes in the eye, such as microaneurysms, retinal hemorrhages, macular edema, and changes in the retinal blood vessels. Detection of microaneurysms (MA) plays a vital role in the early diagn
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Shamela Rizwana, M., J. Gowthamy, and P. Suganya. "True Microaneurysms Detection in Cluttered Retinal Photographs for the Detection of Diabetic Retinopathy." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1985–89. http://dx.doi.org/10.1166/jctn.2020.8477.

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Microaneurysms are the first clinical sign of diabetic retinopathy. The number of microaneurysms is used to indicate the severity of the disease. Early microaneurysm detection can help reduce the incidence of blindness. This paper analyzes the Threshold based technique and Wavelet Decomposition technique to reject specific classes of noises while passing majority of true Microaneurysms using a set of specialized features. The rejection strategy is formulated based on the occurrence frequency and discriminability of the underlying clutter. Threshold based technique separates both classes and fu
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3

Zhou, Wei, Chengdong Wu, Dali Chen, Zhenzhu Wang, Yugen Yi, and Wenyou Du. "Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning." Computational and Mathematical Methods in Medicine 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2483137.

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Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among mul
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4

Zhang, Lizong, Shuxin Feng, Guiduo Duan, Ying Li, and Guisong Liu. "Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism." Genes 10, no. 10 (2019): 817. http://dx.doi.org/10.3390/genes10100817.

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Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the
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Sharma, Aditya, and Vikul Pawar. "Early Assessment of Diabetic Retinopathy by Detecting Microaneurysm with Deep Neural Network." Indian Journal Of Science And Technology 17, no. 17 (2024): 1786–90. http://dx.doi.org/10.17485/ijst/v17i17.732.

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Objectives: This study aims to detect early signs of diabetic retinopathy, also known as microaneurysm (MA). Early detection of MA can help in diagnosing diabetic retinopathy effectively. Methods: To achieve this objective a method is proposed which is based on a deep learning model that incorporates transfer learning. The dataset used in this study is E-Ophtha which consists of 381 high-quality images. The proposed model consists of three steps which are preprocessing, feature extraction and classification. The method uses CLAHE to enhance the details of the fundus image. The feature extracti
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6

G, R. Hemalakshmi, and B. Prakash N. "Detection of Microaneurysms in Fundus Images using ELM Classifier." International Journal of Current Pharmaceutical Review and Research 9, no. 3 (2017): 223–27. https://doi.org/10.5281/zenodo.12674394.

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The main objective of this paper is to detect the microaneurysms which is the sign and symptom for the retinal diseaseDiabetic Retinopathy (DR). In this work, the input image is preprocessed and then cross sectional scanning is applied forpeak detection and property measurement. Feature set from the processed images is extracted and ELM classifier is usedfor MA detection. The experimental results show the proposed system provides better results compared to existing system.
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7

G, R. Hemalakshmi, and B. Prakash N. "Detection of Microaneurysms in Fundus Images using ELM Classifier." International Journal of Current Pharmaceutical Review and Research 8, no. 3 (2017): 223–27. https://doi.org/10.5281/zenodo.12677464.

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Abstract:
The main objective of this paper is to detect the microaneurysms which is the sign and symptom for the retinal diseaseDiabetic Retinopathy (DR). In this work, the input image is preprocessed and then cross sectional scanning is applied forpeak detection and property measurement. Feature set from the processed images is extracted and ELM classifier is usedfor MA detection. The experimental results show the proposed system provides better results compared to existing system.
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8

Aditya, Sharma, and Pawar Vikul. "Early Assessment of Diabetic Retinopathy by Detecting Microaneurysm with Deep Neural Network." Indian Journal of Science and Technology 17, no. 17 (2024): 1786–90. https://doi.org/10.17485/IJST/v17i17.732.

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Abstract <strong>Objectives:</strong>&nbsp;This study aims to detect early signs of diabetic retinopathy, also known as microaneurysm (MA). Early detection of MA can help in diagnosing diabetic retinopathy effectively.&nbsp;<strong>Methods:</strong>&nbsp;To achieve this objective a method is proposed which is based on a deep learning model that incorporates transfer learning. The dataset used in this study is E-Ophtha which consists of 381 high-quality images. The proposed model consists of three steps which are preprocessing, feature extraction and classification. The method uses CLAHE to enh
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9

T. Monisha Birlin. "Automatic Detection of Microaneurysms Using Modified UNET Architecture." Journal of Information Systems Engineering and Management 10, no. 15s (2025): 131–36. https://doi.org/10.52783/jisem.v10i15s.2436.

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Increasingly, more people throughout the world are suffering from Diabetic Retinopathy (DR). A high blood glucose level damages the retina, which is found at the back of the eye and causes vision loss, leading to DR. The earliest symptoms of DR are referred to as microaneurysms (MAs). Due to their small size, dusky color, and practically round shape, these MAs are easy for ophthalmologists to overlook during physical investigation. In this situation, reliable early MA diagnosis is useful to prevent DR before irreversible blindness. The manual detection of DR is a labor-intensive and time-consu
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10

Li, He, Yixiang Deng, Konstantina Sampani, et al. "Computational investigation of blood cell transport in retinal microaneurysms." PLOS Computational Biology 18, no. 1 (2022): e1009728. http://dx.doi.org/10.1371/journal.pcbi.1009728.

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Microaneurysms (MAs) are one of the earliest clinically visible signs of diabetic retinopathy (DR). MA leakage or rupture may precipitate local pathology in the surrounding neural retina that impacts visual function. Thrombosis in MAs may affect their turnover time, an indicator associated with visual and anatomic outcomes in the diabetic eyes. In this work, we perform computational modeling of blood flow in microchannels containing various MAs to investigate the pathologies of MAs in DR. The particle-based model employed in this study can explicitly represent red blood cells (RBCs) and platel
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Conference papers on the topic "Microaneurysms (MA)"

1

Samama, M., and C. E. Baudoin. "EFFECT OF ASPIRIN AND ASPIRIN COMBINED WITH DIPYRIDAMOLE IN EARLY DIABETIC RETINOPATHY." In XIth International Congress on Thrombosis and Haemostasis. Schattauer GmbH, 1987. http://dx.doi.org/10.1055/s-0038-1643855.

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In a double blind randomised controlled clinical trial the effect of antiplatelet agents (aspirin 330 mg × 3 × day) or in combination with dipyridamole (75 mg 3 × day) versus placebo, was tested in 475 patients with early diabetic retinopathy. Patients were follewed fourmonthly for 3 years. Ophtalmological examinations were carried out initially and at yearly intervals. The assessment of retinopathy was based on changes in the number of microaneurysms (MA) present in the macular field as seen on fluorescein angiograms over a period of three years. Forty one patients did not complete the study.
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2

Zhang, Bob, Lei Zhang, Jane You, and Fakhri Karray. "Microaneurysm (MA) Detection via Sparse Representation Classifier with MA and Non-MA Dictionary Learning." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.77.

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