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<p>This work introduces the MSFAUMobileNet model, a complex U-Net structure tailored for retinal blood vessel segmentation, which is a critical process for detecting and monitoring retinal diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration (AMD). The model uses Multi-Scale Feature Aggregation (MSFA), Residual Connections, and Attention Mechanisms to enhance its segmentation accuracy. Utilizing MobileNetV2 as the encoder, the model is capable of effectively generating 13 bottleneck layers’ worth of hierarchical features. Although residual connections and attention mechanisms are useful in improving the segmentation process and guaranteeing the precise outlining of intricate vascular networks, MSFA extracts spatial information at various resolutions. The model was tested on the DRIVE dataset and produced exceptionally high scores with accuracy at 99.99%, Dice coefficient at 99.95%, and Intersection over Union (IoU) at 99.94%. These scores show how efficiently the model separates the complex retinal network, enabling early treatment and detection of retinal disease. MSFAUMobileNet is a good medical image analysis software for real clinical practice owing to its computational speed and precision, particularly in the management of retinal disease.</p>

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Retinal vessel segmentation using multi scale feature attention with MobileNetV2 encoder

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Tanishq Soni,
  • Sheifali Gupta,
  • Salil Bharany,
  • Ateeq Ur Rehman,
  • Rania M. Ghoniem,
  • Belayneh Matebie Taye

摘要

This work introduces the MSFAUMobileNet model, a complex U-Net structure tailored for retinal blood vessel segmentation, which is a critical process for detecting and monitoring retinal diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration (AMD). The model uses Multi-Scale Feature Aggregation (MSFA), Residual Connections, and Attention Mechanisms to enhance its segmentation accuracy. Utilizing MobileNetV2 as the encoder, the model is capable of effectively generating 13 bottleneck layers’ worth of hierarchical features. Although residual connections and attention mechanisms are useful in improving the segmentation process and guaranteeing the precise outlining of intricate vascular networks, MSFA extracts spatial information at various resolutions. The model was tested on the DRIVE dataset and produced exceptionally high scores with accuracy at 99.99%, Dice coefficient at 99.95%, and Intersection over Union (IoU) at 99.94%. These scores show how efficiently the model separates the complex retinal network, enabling early treatment and detection of retinal disease. MSFAUMobileNet is a good medical image analysis software for real clinical practice owing to its computational speed and precision, particularly in the management of retinal disease.