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Adaptive Learning for Diabetic Retinopathy Detection by m_abdullah_umar is a document available to read on EtoBox.

This paper presents a segment-based learning approach for the detection of diabetic retinopathy, which is a leading cause of blindness worldwide. The proposed method utilizes a pre-trained convolutional neural network (CNN) to classify retinal images and identify lesions, achieving a high area under the ROC curve of 0.963 and sensitivity and specificity of 96.37% on the Kaggle dataset. The approach aims to improve the performance of existing methods by integrating classifiers and features from the data, fac

Author

m_abdullah_umar

Language

EN