mlsanityData to 14 Oct 2026

Channel Attention Separable Convolution Network for Skin Lesion Segmentation

arXiv 2309.01072 ↗First posted 3 Sep 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Skin cancer is a frequently occurring cancer in the human population, and it is very important to be able to diagnose malignant tumors in the body early. Lesion segmentation is crucial for monitoring the morphological changes of skin lesions, extracting features to localize and identify diseases to assist doctors in early diagnosis. Manual de-segmentation of dermoscopic images is error-prone and time-consuming, thus there is a pressing demand for precise and automated segmentation algorithms. Inspired by advanced mechanisms such as U-Net, DenseNet, Separable Convolution, Channel Attention, and Atrous Spatial Pyramid Pooling (ASPP), we propose a novel network called Channel Attention Separable Convolution Network (CASCN) for skin lesions segmentation. The proposed CASCN is evaluated on the PH2 dataset with limited images. Without excessive pre-/post-processing of images, CASCN achieves state-of-the-art performance on the PH2 dataset with Dice similarity coefficient of 0.9461 and accuracy of 0.9645.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 3 Sep 2023.

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References · 16

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4 more
  1. 2014Rigid-Motion Scattering for Texture Classificationcited by 52
  2. 2019DSNet: Automatic Dermoscopic Skin Lesion Segmentationcited by 22
  3. 2018A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentationcited by 12
  4. 2020Channel Attention Residual U-Net for Retinal Vessel Segmentationcited by 12

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