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2024 | OriginalPaper | Chapter

Channel Attention Separable Convolution Network for Skin Lesion Segmentation

Authors : Changlu Guo, Jiangyan Dai, Márton Szemenyei, Yugen Yi

Published in: Neural Information Processing

Publisher: Springer Nature Singapore

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Abstract

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.

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National Cancer Institute, Melanoma of the Skin - Cancer Stat Facts (2023). www.seer.cancer.gov/statfacts/html/melan.html

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Title: Channel Attention Separable Convolution Network for Skin Lesion Segmentation
Authors: Changlu Guo
Jiangyan Dai
Márton Szemenyei
Yugen Yi
Publisher: Springer Nature Singapore
Book: Neural Information Processing
Print ISBN: 978-981-9980-66-6

Electronic ISBN: 978-981-9980-67-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-8067-3_29

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