Papers › iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation

iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation

13 Apr 2023International Conference on Neural Information Processing 2023 4archive 2025-07-28

Proma Hossain Progga, Swakkhar Shatabda

In this paper, we propose iResSENet, a novel deep learning-based image segmentation model 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. The proposed method was applied for segmentation tasks in retinal blood vessels. The experimental results show that the proposed method is significantly superior compared to existing methods on several standard benchmark datasets.

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promaprogga/iResSENet-An-Accurate-Convolutional-Neural-Network-for-Retinal-Blood-Vessel-Segmentation tf report

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Image Segmentation Segmentation Semantic Segmentation

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Methods

Concatenated Skip Connection Convolution Max Pooling ReLU U-Net

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