FM-Net: Focal Modulation-based Network forAccurate Skin Lesion Segmentation

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 28, 2025

Precise segmentation of skin lesions in dermoscopic images is critical for cancers, including melanoma, as accurate delineation essential timely and effective diagnosis. Cancerous lesions, particularly malignant significantly contribute to the mortality rate underscoring need early detection precise diagnostic techniques. However, achieving this precision poses challenges due indistinct lesion borders, asymmetrical shapes, common obstructions like hair, markings, occlusions. This study addresses these by introducing an end-to-end trainable network incorporating focal modulation enhance feature refinement pixel-level classification. The captures fine-grained multi-scale features contextual information lesions. decoder part proposed method utilizes transposed convolution up-sampling, which preserves spatial detail necessary high-resolution segmentation. achieves state-of-the-art (SOTA) performance breast segmentation, validated across multiple benchmark datasets. An outstanding its ability deliver without employing data augmentation. robustness demonstrated on ISIC datasets, Jaccard index scores 89.60% 2016, 82.34% 2017, 87.71% 2018. Moreover, computed ultrasound (BUSI) dataset. comprehensive our highlights accurately segment potential assist code reproduce results made available at \href{https://github.com/Asim-Naveed/FM-Nets}{GitHub}.

Язык: Английский

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A Review of U‐Net‐Based Deep Learning Models for Skin Lesion Segmentation

International Journal of Imaging Systems and Technology, Год журнала: 2025, Номер 35(3)

Опубликована: Май 1, 2025

ABSTRACT Automated skin lesion segmentation is crucial for early and accurate cancer diagnosis. Deep learning, particularly U‐Net, has revolutionized the field of automatic segmentation. This review comprehensively examines U‐Net its variants employed automated It outlines foundational architecture explores diverse architectural innovations, including attention mechanisms, advanced skip connections, residual dilated convolutions, transformer models, hybrid models. The highlights how these adaptations address inherent challenges in segmentation, data limitations heterogeneity. also discusses commonly used datasets, evaluation metrics, compares model performance computational cost. Finally, it addresses existing future research directions to advance

Язык: Английский

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MSCB-UNet : Elevating skin lesion segmentation performance with Multi-scale Spatial-Channel Bridging Network

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 110, С. 107986 - 107986

Опубликована: Май 23, 2025

Язык: Английский

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An attention-guided multi-scale fusion network for surgical instrument segmentation

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 102, С. 107296 - 107296

Опубликована: Дек. 4, 2024

Язык: Английский

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