AI / ML · 2024
LMBiS-Net
An implementation of Abbasi et al.'s “LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation.”
Project Goal
To implement the LMBiS-Net model and confirm the findings presented in the original paper. Additionally, we aimed to apply our implementation to a different dataset that was not used in the paper.
Why It Matters
LMBiS-Net's primary benefit is an accurate retinal blood-vessel segmentation model that is computationally efficient compared to state-of-the-art models. This efficiency can assist ophthalmologists in the early detection and treatment of retinal diseases, reducing manual effort and potential human error.
Retinal diseases are a major cause of visual impairment and blindness — studies show that 5%–20% of the global population aged 40+ has retinal disorders. Examining retinal vessels provides critical insight into the underlying conditions that contribute to these diseases.
The Model
LMBiS-Net is a CNN consisting of three encoder blocks, a bottleneck layer, and three decoder blocks. It uses multipath feature-extraction blocks and bidirectional skip connections to enhance information flow between the encoders and decoders.
Multi-Path Feature Extraction
This component introduces feature diversity into the model, reducing overfitting and improving generalization. By using different-sized convolutions, the network captures both low-level and high-level features crucial for blood-vessel segmentation.
Our Contribution
We created the first publicly available implementation of LMBiS-Net and developed code to augment retinal images, increasing the size of training datasets. Our findings support the original paper's claims that LMBiS-Net is a computationally efficient and accurate state-of-the-art model for retinal blood-vessel segmentation.
Results
