The Epidemiology of Automatic Skin Cancer Detection by Comparative Analysis of Pre-processing and Segmentation Techniques

Thapar, Puneet; Rakhra, Manik; Singh, Aman

doi:10.1109/iciem54221.2022.9853163

Cited by 10 publications

(1 citation statement)

References 35 publications

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“…Moreover, the types of lesions vary greatly among different samples. Fortunately, the rapid evolution of deep learning technology has paved the way for innovative solutions to address these challenges [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention

Xue,

Chen,

Liu

et al. 2024

PLoS ONE

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Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively.

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Section: Introductionmentioning

confidence: 99%