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Journal of Medical Signals and Sensors، جلد ۱۲، شماره ۲، صفحات ۱۰۸-۱۱۳
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Weight pruning-UNet: Weight pruning UNet with depth-wise separable convolutions for semantic segmentation of kidney tumors |
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| چکیده انگلیسی مقاله |
Background: Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 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 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e 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. |
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| کلیدواژه‌های انگلیسی مقاله |
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| نویسندگان مقاله |
| Patike Kiran Rao
Department of Computer Science and Engineering, Faculty of Engineering and Technology, MS Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India
| Subarna Chatterjee
Department of Nephrology, Kurnool Medical College, Kurnool, Andra Pradesh, India
| Sreedhar Sharma
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| نشانی اینترنتی |
http://jmss.mui.ac.ir/index.php/jmss/article/view/610 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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| نوع مقاله منتشر شده |
Original Articles |
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