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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Seg-SkiNet: adaptive deformable fusion convolutional network for skin lesion segmentation.

Haiwang Nan¹, Zhenhao Gao¹, Limei Song²

¹School of Computer and Control Engineering, Yantai University, Yantai, China.

Quantitative Imaging in Medicine and Surgery

|January 22, 2025

Summary

This study introduces Seg-SkiNet, a deep learning model for precise skin lesion segmentation. The model excels at accurately segmenting complex shapes and small lesions, crucial for early skin cancer diagnosis.

Keywords:

Skin lesion segmentation U-Net deep learning (DL)multi-scale feature

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Precise skin lesion segmentation is vital for accurate skin cancer diagnosis.
Challenges include complex lesion shapes, varying sizes, and color depths.
Existing methods struggle with intricate lesion features and small targets.

Purpose of the Study:

To develop a customized deep learning (DL) model for precise skin lesion segmentation.
To specifically address challenges posed by complex shapes and small target lesions.
To improve diagnostic accuracy through enhanced segmentation capabilities.

Main Methods:

Proposed an adaptive deformable fusion convolutional network (Seg-SkiNet).
Integrated a Dual-Channel Convolution Encoder (Dual-Conv encoder) for edge and internal feature capture.
Employed a Multi-Scale-Multi-Receptive Field Extraction and Refinement (Multi²ER) module for small lesion segmentation and a Local-Global Information Interaction Fusion Decoder (LGI-FSN decoder) for feature fusion.

Main Results:

Seg-SkiNet achieved high performance on public datasets (ISIC-2016, ISIC-2017, ISIC-2018).
Demonstrated Dice coefficients of 93.66%, 89.44%, and 92.29% respectively.
Validated effectiveness in segmenting challenging complex-shaped and small skin lesions.

Conclusions:

Seg-SkiNet shows excellent performance in segmenting complex-shaped lesions.
The model is highly effective for segmenting small target skin lesions.
This advancement contributes to more accurate skin cancer diagnosis through improved segmentation.