LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation

• URL: http://arxiv.org/abs/2407.02871v1
• Date: Wed, 3 Jul 2024 07:37:09 GMT
• Title: LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation
• Authors: Tariq M Khan, Shahzaib Iqbal, Syed S. Naqvi, Imran Razzak, Erik Meijering,
• Abstract summary: Retinal diseases can cause irreversible vision loss in both eyes if not diagnosed and treated early. Current deep learning techniques for segmenting retinal images with many labels and attributes have poor detection accuracy and generalisability. This paper presents a multipath convolutional neural network for multifeature segmentation.
• Score: 15.091476025563528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retinal diseases can cause irreversible vision loss in both eyes if not diagnosed and treated early. Since retinal diseases are so complicated, retinal imaging is likely to show two or more abnormalities. Current deep learning techniques for segmenting retinal images with many labels and attributes have poor detection accuracy and generalisability. This paper presents a multipath convolutional neural network for multifeature segmentation. The proposed network is lightweight and spatially sensitive to information. A patch-based implementation is used to extract local image features, and focal modulation attention blocks are incorporated between the encoder and the decoder for improved segmentation. Filter optimisation is used to prevent filter overlaps and speed up model convergence. A combination of convolution operations and group convolution operations is used to reduce computational costs. This is the first robust and generalisable network capable of segmenting multiple features of fundus images (including retinal vessels, microaneurysms, optic discs, haemorrhages, hard exudates, and soft exudates). The results of our experimental evaluation on more than ten publicly available datasets with multiple features show that the proposed network outperforms recent networks despite having a small number of learnable parameters.

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