This repository contains the original implementation of "iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation".

• Email: [email protected]

This code implements the paper:

Progga, P.H., Shatabda, S. (2023). iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation. In Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_48

If you find this work is helpful for your research, please cite our paper [PDF].

• View the Preprint

We share this code only for research use. If you find any problem or inappropriate content in this code, feel free to contact.

Download the Retinal datasets and their masks: DRIVE (Link), CHASE_DB1 (Link), HRF (Link) and STARE (Link).

iResSENet, a novel deep learning-based architecture based on U-Net architecture. The proposed method enhances U-Net in three aspects. It replaces the encoder blocks with residual connections in addition to 1×1 convolutional layers and channel-based attention.

Model code available in here (tensorflow)

If you use 'iResSENet' in your project, please cite the following paper-

@inproceedings{progga2023iressenet,
  title={iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation},
  author={Progga, Proma Hossain and Shatabda, Swakkhar},
  booktitle={Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22--26, 2022, Proceedings, Part III},
  pages={567--578},
  year={2023},
  organization={Springer}
}