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Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images by Law Kumar Singh; Munish Khanna; Shankar Thawkar; Rekha Singh is a Computer Science article available to read on EtoBox.

What is Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images about?

The segmentation of blood vessels through color fundus images is a difficult and timeconsuming task that requires experienced clinicians. Recently, researchers have shown that blood vessel segmentation using methods based on deep neural networks has achieved highly satisfactory results. This motivates us to employ a fast and accurate deep learningbased method that can be used in blood vessel segmentation. Five improved deep learningbased networks (U-Net, DenseU-Net, LadderNet, R2U-Net, and ATTU-Net), along with an enhanced customized R2-ATT U-Net deep learning network, have been employed to segment the retinal blood vessel tree. Segmentation using patches extraction is executed, where we have used 10,000 patches per image, in total, 8000 for training and 2000 for testing public benchmark STARE dataset images. Initially, we performed the training, followed by testing for all six models using the patch extraction approach. All the aspects of the testing phase (test log, ROC curve, precision recall curves, and confusion matrix) and statistical performance measuring metrics (accuracy, sensitivity, specificity, F1 score, precision, and AUC values) are covered in this work and are also s

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Author

Law Kumar Singh; Munish Khanna; Shankar Thawkar; Rekha Singh

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)