Name
Title | ||
|---|---|---|
FF-UNet: a U-Shaped Deep Convolutional Neural Network for Multimodal Biomedical Image Segmentation |
Abstract | ||
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Automatic multimodal image segmentation is considered a challenging research area in the biomedical field. U-shaped models have led to an enormous breakthrough in a large domain of medical image segmentation in recentyears. The receptive field plays an essential role in convolutionalneural networks because too small a receptive field limits context information, and too large loses localization accuracy. Despite outstanding overall performance in biomedical segmenting, classical UNet architecture uses a fixed receptive field in convolutions operations. This study proposes a few modifications in classical UNet architecture by adjusting the receptive field via feature-fused module and attention gate mechanism. Compared with baseline UNet, the numerical parameters of FF-UNet (3.94 million) is 51% of classical UNet architecture (7.75 million). Furthermore, we extended our model performance by introducing post-processing schemes. The tri-threshold fuzzy intensification-based contrast enhancement technique is utilized to improve the contrast of biomedical datasets. In the second tier, the black top-hat filtering-based method is employed to remove hair-like artifacts from the ISIC 2018 skin lesion dataset, which may create a barrier to correctly segmenting the images. The proposed models have been trained using fivefold cross-validation on five publicly available biomedical datasets and achieved the dice coefficients of 0.860, 0.932, 0.932, 0.925, and 0.894 on ETIS-LaribPolypDB, CVC-ColonDB, CVC-ClinicDB, DSB 2018, and ISIC 2018 datasets, respectively. To further verify our claims, comparative analysis based on dice results is conducted, proving the proposed model effectiveness. The FF-UNet implementation models and pre-trained weights are freely publicly available:
https://github.com/ahmedeqbal/FF-UNet
.
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Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s12559-022-10038-y | Cognitive Computation |
Keywords | DocType | Volume |
Biomedical image segmentation, Convolutional neural networks, U-shaped networks, Receptive fields, Attention mechanism | Journal | 14 |
Issue | ISSN | Citations |
4 | 1866-9956 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Iqbal | 1 | 0 | 0.68 |
| Muhammad Sharif | 2 | 317 | 37.96 |
| Muhammad Attique Khan | 3 | 47 | 9.72 |
| Wasif Nisar | 4 | 0 | 0.34 |
| Majed Alhaisoni | 5 | 0 | 0.34 |