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• W3133034745 abstract "Diabetes is one of the leading health problem all over the world. Microaneurysms (MAs) are the initial diabetic retinopathy (DR) signs that are detectable at the clinical level in human. Diagnosis and treatment of DR is an important task to be performed. Many methods for identifying microaneurysms, such as: ANN, CNN and AI, have been previously suggested. In this work, we propose machine learning based classifiers to identify MAs with low computational power. The retinal images have been segmented on the basis of histograms. Then, features have been extracted from segmented images using shape, statistical and gray-level co-occurrence matrix (GLCM) methods. Thereafter, classification algorithms: Logistics regression, k-NN, SVM, Kernel SVM, Naive Bayes, decision tree, and random forest have been applied for classifying MAs versus non-MAs. The proposed model obtained accuracy of 69.7% 76.04%, 68.75%, 76.04%, 58.33%, 73.958%, and 83.33% respectively for different classification algorithms i.e. random forest outperformed the other methods with an accuracy of 83.33%." @default.
• W3133034745 created "2021-03-01" @default.
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• W3133034745 date "2021-05-01" @default.
• W3133034745 modified "2023-10-06" @default.
• W3133034745 title "Microaneurysm detection using color locus detection method" @default.
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• W3133034745 doi "https://doi.org/10.1016/j.measurement.2021.109084" @default.
• W3133034745 hasPublicationYear "2021" @default.
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