Weight Pruning‑UNet: Weight Pruning UNet with Depth‑wise Separable Convolutions for Semantic Segmentation of Kidney Tumors

Background: Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs۱۹ challenge sets the groundwork for future advances in kidney tumor segmentation. Methods: We present weight pruning (WP)‑UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating‑point computational complexity. Results: We trained and evaluated the model with CT images from ۲۱۰ patients. The findings implied the dominance of our method on the training Dice score (۰.۹۸) for the kidney tumor region. The proposed model only uses ۱,۲۹۷,۴۴۱ parameters and ۷.۲e floating‑point operations, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.

کلیدواژه ها:

Depth‑wise separable convolution ، kidney ، kidney tumor segmentation ، pruning ، weight pruning‑UNet