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

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Abstract

The segmentation of blood vessels through color fundus images is a difficult and time-consuming 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 learning-based method that can be used in blood vessel segmentation. Five improved deep learning-based 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 shown in the form of tables and graphs. An in-depth performance evaluation analysis of these six implemented improved nets has been performed to evaluate the segmentation process. We got the best result in the case of LadderNet, with an accuracy of 0.971, which is comparable to recent state-of-the-art studies. In terms of accuracy, the enhanced customized R2-ATT U-Net deep learning network is also highly satisfactory, being near the best model, LadderNet. The experiment results demonstrate that the proposed methodology achieves satisfactory performance in the retinal blood vessel extraction domain and can help ophthalmologists predict many eye-related diseases at a preliminary stage.

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Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Mathura, India, 2821406

    Law Kumar Singh

  2. Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India, 281122

    Munish Khanna & Shankar Thawkar

  3. Department of Physics, U.P. Rajarshi Tandon Open University, Prayagraj, India

    Rekha Singh

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f.The dataset analysed during the current study are available in the internet repository, and can also be made available from the corresponding author on reasonable request

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Singh, L.K., Khanna, M., Thawkar, S. et al. Deep-learning based system for effective and automatic blood vessel segmentation from Retinal fundus images. Multimed Tools Appl 83, 6005–6049 (2024). https://doi.org/10.1007/s11042-023-15348-3

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