U-net based polyp segmentation on kvasir-seg dataset: performance evaluation and comparison with state-of-the-art methods

Arabboev, Mukhriddin, Begmatov, Shohruh, Bobojanov, Sukhrob

Муҳандислик ва Иқтисодиёт · 2025-yil

Annotatsiya

Medical image segmentation is a fundamental task in computer-assisted diagnosis, enabling accuratedelineation of anatomical structures and pathological regions. In gastrointestinal endoscopy, precise segmentation ofpolyps is crucial for early detection and treatment planning. This study presents a U-Net-based approach for polypsegmentation using the publicly available Kvasir-SEG dataset. The proposed model was trained for 100 epochs withcustom metrics including Dice coefficient, Intersection over Union (IoU), precision, and recall. Experimental resultsdemonstrate that the model achieved an average Dice score of 0.9449, IoU of 0.9084, precision of 0.9404, and recall of0.9584, outperforming several recent state-of-the-art methods. Comparative analysis with existing approaches confirmsthe effectiveness of the vanilla U-Net architecture when combined with careful preprocessing and hyperparameter tuning.The findings highlight U-Net's continued relevance in medical image segmentation tasks and suggest directions for futurework, including integration with attention mechanisms and transformer-based architectures.

Maqola ma’lumotlari
MualliflarArabboev, Mukhriddin, Begmatov, Shohruh, Bobojanov, Sukhrob
JurnalМуҳандислик ва Иқтисодиёт
NashriyotПлеханов номидаги Россия иқтисодиёти университетининг Тошкент шаҳридаги филиали
ISSN3060-463X, 3060-463X
Nashr sanasi2025-12-01
Jild3
Son12
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

U-Net, Polyp Segmentation, Kvasir-SEG, Deep Learning, Medical Image Segmentation, Convolutional Neural Networks.

Ilmiy soha

Texnika

Iqtibos keltirish

APAArabboev, Mukhriddin, Begmatov, Shohruh, & Bobojanov, Sukhrob (2025). U-NET BASED POLYP SEGMENTATION ON KVASIR-SEG DATASET: PERFORMANCE EVALUATION AND COMPARISON WITH STATE-OF-THE-ART METHODS. Муҳандислик ва Иқтисодиёт, 3(12). https://muhandislik-iqtisodiyot.uz/index.php/journal/article/view/1509
GOST (OAK)Arabboev, Mukhriddin, Begmatov, Shohruh, Bobojanov, Sukhrob U-NET BASED POLYP SEGMENTATION ON KVASIR-SEG DATASET: PERFORMANCE EVALUATION AND COMPARISON WITH STATE-OF-THE-ART METHODS // Муҳандислик ва Иқтисодиёт. – 2025. – T. 3, № 12.
MLAArabboev, Mukhriddin, Begmatov, Shohruh, and Bobojanov, Sukhrob. “U-NET BASED POLYP SEGMENTATION ON KVASIR-SEG DATASET: PERFORMANCE EVALUATION AND COMPARISON WITH STATE-OF-THE-ART METHODS.” Муҳандислик ва Иқтисодиёт, 3(12), 2025. https://muhandislik-iqtisodiyot.uz/index.php/journal/article/view/1509
ChicagoArabboev, Mukhriddin, Begmatov, Shohruh, and Bobojanov, Sukhrob. “U-NET BASED POLYP SEGMENTATION ON KVASIR-SEG DATASET: PERFORMANCE EVALUATION AND COMPARISON WITH STATE-OF-THE-ART METHODS.” Муҳандислик ва Иқтисодиёт, 3(12) (2025). https://muhandislik-iqtisodiyot.uz/index.php/journal/article/view/1509
HarvardArabboev, Mukhriddin, Begmatov, Shohruh, and Bobojanov, Sukhrob (2025) ‘U-NET BASED POLYP SEGMENTATION ON KVASIR-SEG DATASET: PERFORMANCE EVALUATION AND COMPARISON WITH STATE-OF-THE-ART METHODS’, Муҳандислик ва Иқтисодиёт, 3(12). https://muhandislik-iqtisodiyot.uz/index.php/journal/article/view/1509
BibTeX
@article{arabboevmukhriddin2025,
  title = {U-NET BASED POLYP SEGMENTATION ON KVASIR-SEG DATASET: PERFORMANCE EVALUATION AND COMPARISON WITH STATE-OF-THE-ART METHODS},
  author = {Arabboev, Mukhriddin and Begmatov, Shohruh and Bobojanov, Sukhrob},
  journal = {Муҳандислик ва Иқтисодиёт},
  year = {2025},
  volume = {3},
  number = {12},
  url = {https://muhandislik-iqtisodiyot.uz/index.php/journal/article/view/1509}
}

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